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Atomizer/studies/m1_mirror_zernike_optimization/2_results/study.db

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feat: Add Protocol 13 adaptive optimization, Plotly charts, and dashboard improvements ## Protocol 13: Adaptive Multi-Objective Optimization - Iterative FEA + Neural Network surrogate workflow - Initial FEA sampling, NN training, NN-accelerated search - FEA validation of top NN predictions, retraining loop - adaptive_state.json tracks iteration history and best values - M1 mirror study (V11) with 103 FEA, 3000 NN trials ## Dashboard Visualization Enhancements - Added Plotly.js interactive charts (parallel coords, Pareto, convergence) - Lazy loading with React.lazy() for performance - Code splitting: plotly.js-basic-dist (~1MB vs 3.5MB) - Chart library toggle (Recharts default, Plotly on-demand) - ExpandableChart component for full-screen modal views - ConsoleOutput component for real-time log viewing ## Documentation - Protocol 13 detailed documentation - Dashboard visualization guide - Plotly components README - Updated run-optimization skill with Mode 5 (adaptive) ## Bug Fixes - Fixed TypeScript errors in dashboard components - Fixed Card component to accept ReactNode title - Removed unused imports across components 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-12-04 07:41:54 -05:00
SQLite format 3@ <01>V<01>.<2E><><0F><0F> <0C> <0A>  E f
feat: Add M1 mirror Zernike optimization with correct RMS calculation Major improvements to telescope mirror optimization workflow: Assembly FEM Workflow (solve_simulation.py): - Fixed multi-part assembly FEM update sequence - Use ImportFromFile() for reliable expression updates - Add DuplicateNodesCheckBuilder with MergeOccurrenceNodes=True - Switch to Foreground solve mode for multi-subcase solutions - Add detailed logging and diagnostics for node merge operations Zernike RMS Calculation: - CRITICAL FIX: Use correct surface-based RMS formula - Global RMS = sqrt(mean(W^2)) from actual WFE values - Filtered RMS = sqrt(mean(W_residual^2)) after removing low-order fit - This matches zernike_Post_Script_NX.py (optical standard) - Previous WRONG formula was: sqrt(sum(coeffs^2)) - Add compute_rms_filter_j1to3() for optician workload metric Subcase Mapping: - Fix subcase mapping to match NX model: - Subcase 1 = 90 deg (polishing orientation) - Subcase 2 = 20 deg (reference) - Subcase 3 = 40 deg - Subcase 4 = 60 deg New Study: M1 Mirror Zernike Optimization - Full optimization config with 11 design variables - 3 objectives: rel_filtered_rms_40_vs_20, rel_filtered_rms_60_vs_20, mfg_90_optician_workload - Neural surrogate support for accelerated optimization Documentation: - Update ZERNIKE_INTEGRATION.md with correct RMS formula - Update ASSEMBLY_FEM_WORKFLOW.md with expression import and node merge details - Add reference scripts from original zernike_Post_Script_NX.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-28 16:30:15 -05:00
<EFBFBD>K<07>d<07><04>a<04><01>(<01><01>Qe?indexsqlite_autoindex_trial_intermediate_values_1trial_intermediate_values<16>E%%<01>Mtabletrial_valuestrial_valuesCREATE TABLE trial_values (
trial_value_id INTEGER NOT NULL,
trial_id INTEGER NOT NULL,
objective INTEGER NOT NULL,
value FLOAT,
value_type VARCHAR(7) NOT NULL,
PRIMARY KEY (trial_value_id),
UNIQUE (trial_id, objective),
FOREIGN KEY(trial_id) REFERENCES trials (trial_id)
)7K%indexsqlite_autoindex_trial_values_1trial_values<14>,%%<01>tabletrial_paramstrial_paramsCREATE TABLE trial_params (
param_id INTEGER NOT NULL,
trial_id INTEGER,
param_name VARCHAR(512),
param_value FLOAT,
distribution_json TEXT,
PRIMARY KEY (param_id),
UNIQUE (trial_id, param_name),
FOREIGN KEY(trial_id) REFERENCES trials (trial_id)
)7K%indexsqlite_autoindex_trial_params_1trial_params<12>I;;<01>)tabletrial_system_attributestrial_system_attributesCREATE TABLE trial_system_attributes (
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FOREIGN KEY(trial_id) REFERENCES trials (trial_id)
)Ma;indexsqlite_autoindex_trial_system_attributes_1trial_system_attributes<10>? 77<01>tabletrial_user_attributestrial_user_attributes CREATE TABLE trial_user_attributes (
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trial_id INTEGER,
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value_json TEXT,
PRIMARY KEY (trial_user_attribute_id),
UNIQUE (trial_id, "key"),
FOREIGN KEY(trial_id) REFERENCES trials (trial_id)
)I ]7indexsqlite_autoindex_trial_user_attributes_1trial_user_attributesX 1uindexix_trials_study_idtrials CREATE INDEX ix_trials_study_id ON trials (study_id)<29>
<01> tabletrialstrials CREATE TABLE trials (
trial_id INTEGER NOT NULL,
number INTEGER,
study_id INTEGER,
state VARCHAR(8) NOT NULL,
datetime_start DATETIME,
datetime_complete DATETIME,
PRIMARY KEY (trial_id),
FOREIGN KEY(study_id) REFERENCES studies (study_id)
)<29>J;;<01>+tablestudy_system_attributesstudy_system_attributes CREATE TABLE study_system_attributes (
study_system_attribute_id INTEGER NOT NULL,
study_id INTEGER,
"key" VARCHAR(512),
value_json TEXT,
PRIMARY KEY (study_system_attribute_id),
UNIQUE (study_id, "key"),
FOREIGN KEY(study_id) REFERENCES studies (study_id)
)M a;indexsqlite_autoindex_study_system_attributes_1study_system_attributes
<EFBFBD>@77<01>tablestudy_user_attributesstudy_user_attributesCREATE TABLE study_user_attributes (
study_user_attribute_id INTEGER NOT NULL,
study_id INTEGER,
"key" VARCHAR(512),
value_json TEXT,
PRIMARY KEY (study_user_attribute_id),
UNIQUE (study_id, "key"),
FOREIGN KEY(study_id) REFERENCES studies (study_id)
)I]7indexsqlite_autoindex_study_user_attributes_1study_user_attributes<08>J--<01>Gtablestudy_directionsstudy_directionsCREATE TABLE study_directions (
study_direction_id INTEGER NOT NULL,
direction VARCHAR(8) NOT NULL,
study_id INTEGER NOT NULL,
objective INTEGER NOT NULL,
PRIMARY KEY (study_direction_id),
UNIQUE (study_id, objective),
FOREIGN KEY(study_id) REFERENCES studies (study_id)
)?S-indexsqlite_autoindex_study_directions_1study_directions<00>\%%<01>{tableversion_infoversion_infoCREATE TABLE version_info (
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schema_version INTEGER,
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PRIMARY KEY (version_info_id),
CHECK (version_info_id=1)
)j7<01>indexix_studies_study_namestudiesCREATE UNIQUE INDEX ix_studies_study_name ON studies (study_name)<29> <01>otablestudiesstudiesCREATE TABLE studies (
study_id INTEGER NOT NULL,
study_name VARCHAR(512) NOT NULL,
PRIMARY KEY (study_ <0F><0F>!Im1_m
<0F><0F>!I m1_mirror_zernike_optimization
 4.6.0 <0F><0F> 
<0F><0F> 

feat: Add Protocol 13 adaptive optimization, Plotly charts, and dashboard improvements ## Protocol 13: Adaptive Multi-Objective Optimization - Iterative FEA + Neural Network surrogate workflow - Initial FEA sampling, NN training, NN-accelerated search - FEA validation of top NN predictions, retraining loop - adaptive_state.json tracks iteration history and best values - M1 mirror study (V11) with 103 FEA, 3000 NN trials ## Dashboard Visualization Enhancements - Added Plotly.js interactive charts (parallel coords, Pareto, convergence) - Lazy loading with React.lazy() for performance - Code splitting: plotly.js-basic-dist (~1MB vs 3.5MB) - Chart library toggle (Recharts default, Plotly on-demand) - ExpandableChart component for full-screen modal views - ConsoleOutput component for real-time log viewing ## Documentation - Protocol 13 detailed documentation - Dashboard visualization guide - Plotly components README - Updated run-optimization skill with Mode 5 (adaptive) ## Bug Fixes - Fixed TypeScript errors in dashboard components - Fixed Card component to accept ReactNode title - Removed unused imports across components 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-12-04 07:41:54 -05:00

<EFBFBD>
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p
, <09> <09> ` <08><08>P <07><07>@<06><06>t0<05><05>b<04><04>e:<03><03><03>sK <02><02>)6 A5RUNNING2025-12-01 12:42:01.191743D5 AA4COMPLETE2025-12-01 12:37:20.0434282025-12-01 12:42:01.177619)4 A3RUNNING2025-12-01 11:46:04.279303&3 A2FAIL2025-12-01 11:43:18.951238&2 A1FAIL2025-12-01 11:40:35.645830830&1 A0FAIL2025-12-01 11:38:30.485573573&0 A/FAIL2025-12-01 11:36:06.105182182D/ AA.COMPLETE2025-12-01 11:34:38.0084192025-12-01 11:36:06.077261&. A-FAIL2025-12-01 11:32:27.776867867&- A,FAIL2025-12-01 11:27:44.461477477D, AA+COMPLETE2025-12-01 11:25:04.3064772025-12-01 11:27:44.445280D+ AA*COMPLETE2025-12-01 11:22:29.7052732025-12-01 11:25:04.291503D* AA)COMPLETE2025-12-01 11:19:39.4410732025-12-01 11:22:29.688040D) AA(COMPLETE2025-12-01 10:47:00.7318752025-12-01 10:49:51.691669B( AA'PRUNED2025-12-01 10:43:33.4963272025-12-01 10:45:45.051227B' AA&PRUNED2025-12-01 10:26:38.5643632025-12-01 10:28:53.424040B& AA%PRUNED2025-12-01 10:20:54.3794312025-12-01 10:22:52.756359B% AA$PRUNED2025-12-01 09:45:09.0656152025-12-01 09:47:12.112653B$ AA#PRUNED2025-12-01 09:42:26.2259912025-12-01 09:44:25.236660B# AA"PRUNED2025-12-01 09:39:30.6189592025-12-01 09:41:36.272496B" AA!PRUNED2025-12-01 09:32:56.2809612025-12-01 09:34:55.576810B! AA PRUNED2025-12-01 09:30:36.9556732025-12-01 09:32:25.034239B  AAPRUNED2025-12-01 09:25:39.4779902025-12-01 09:27:40.099196B AAPRUNED2025-12-01 09:21:24.8796902025-12-01 09:23:27.831938B AAPRUNED2025-12-01 09:16:44.1839382025-12-01 09:18:42.312114B AAPRUNED2025-12-01 09:12:33.3341402025-12-01 09:14:36.231129B AAPRUNED2025-12-01 09:00:46.5586982025-12-01 09:03:16.309077B AAPRUNED2025-11-30 12:19:42.0691782025-11-30 12:21:38.209311B AAPRUNED2025-11-30 12:16:43.7205752025-11-30 12:18:35.983027B AAPRUNED2025-11-30 12:13:47.2302292025-11-30 12:15:39.531797B AAPRUNED2025-11-30 12:10:59.1371372025-11-30 12:11:51.086239B AAPRUNED2025-11-30 12:07:37.5227592025-11-30 12:09:53.199723B AAPRUNED2025-11-29 13:45:34.3661692025-11-29 13:50:16.443527D AACOMPLETE2025-11-29 13:36:06.6795322025-11-29 13:41:50.193115& AFAIL2025-11-28 16:22:00.325406406& AFAIL2025-11-28 16:05:01.952272272& AFAIL2025-11-28 16:01:14.717081081& AFAIL2025-11-28 15:57:06.336836836D AACOMPLETE2025-11-28 15:51:03.3459142025-11-28 15:57:06.324738D AACOMPLETE2025-11-28 15:45:06.6249682025-11-28 15:51:03.330166B AA PRUNED2025-11-28 15:40:04.8202212025-11-28 15:44:37.527244D  AA COMPLETE2025-11-28 15:32:12.5111012025-11-28 15:39:14.046169D  AA COMPLETE2025-11-28 15:06:38.6366262025-11-28 15:14:08.641770D  AA
feat: Add M1 mirror Zernike optimization with correct RMS calculation Major improvements to telescope mirror optimization workflow: Assembly FEM Workflow (solve_simulation.py): - Fixed multi-part assembly FEM update sequence - Use ImportFromFile() for reliable expression updates - Add DuplicateNodesCheckBuilder with MergeOccurrenceNodes=True - Switch to Foreground solve mode for multi-subcase solutions - Add detailed logging and diagnostics for node merge operations Zernike RMS Calculation: - CRITICAL FIX: Use correct surface-based RMS formula - Global RMS = sqrt(mean(W^2)) from actual WFE values - Filtered RMS = sqrt(mean(W_residual^2)) after removing low-order fit - This matches zernike_Post_Script_NX.py (optical standard) - Previous WRONG formula was: sqrt(sum(coeffs^2)) - Add compute_rms_filter_j1to3() for optician workload metric Subcase Mapping: - Fix subcase mapping to match NX model: - Subcase 1 = 90 deg (polishing orientation) - Subcase 2 = 20 deg (reference) - Subcase 3 = 40 deg - Subcase 4 = 60 deg New Study: M1 Mirror Zernike Optimization - Full optimization config with 11 design variables - 3 objectives: rel_filtered_rms_40_vs_20, rel_filtered_rms_60_vs_20, mfg_90_optician_workload - Neural surrogate support for accelerated optimization Documentation: - Update ZERNIKE_INTEGRATION.md with correct RMS formula - Update ASSEMBLY_FEM_WORKFLOW.md with expression import and node merge details - Add reference scripts from original zernike_Post_Script_NX.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-28 16:30:15 -05:00
COMPLETE2025-11-28 14:57:17.4271632025-11-28 15:04:55.607046B
 AA PRUNED2025-11-28 14:56:01.1459942025-11-28 14:56:32.456003D  AACOMPLETE2025-11-28 14:49:07.0722812025-11-28 14:51:51.852600D AACOMPLETE2025-11-28 14:46:32.5243232025-11-28 14:47:33.513325D AACOMPLETE2025-11-28 14:42:34.7803852025-11-28 14:43:38.127670D AACOMPLETE2025-11-28 14:41:31.2960642025-11-28 14:42:34.769020D AACOMPLETE2025-11-28 14:40:25.5596922025-11-28 14:41:31.278895D AACOMPLETE2025-11-28 14:36:25.2164072025-11-28 14:37:26.171708D AACOMPLETE2025-11-28 14:35:23.9650282025-11-28 14:36:25.202369C AACOMPLETE2025-11-28 14:34:21.2945952025-11-28 14:35:23.945387C AACOMPLETE2025-11-28 14:24:35.8505382025-11-28 14:25:37.733649
feat: Add Protocol 13 adaptive optimization, Plotly charts, and dashboard improvements ## Protocol 13: Adaptive Multi-Objective Optimization - Iterative FEA + Neural Network surrogate workflow - Initial FEA sampling, NN training, NN-accelerated search - FEA validation of top NN predictions, retraining loop - adaptive_state.json tracks iteration history and best values - M1 mirror study (V11) with 103 FEA, 3000 NN trials ## Dashboard Visualization Enhancements - Added Plotly.js interactive charts (parallel coords, Pareto, convergence) - Lazy loading with React.lazy() for performance - Code splitting: plotly.js-basic-dist (~1MB vs 3.5MB) - Chart library toggle (Recharts default, Plotly on-demand) - ExpandableChart component for full-screen modal views - ConsoleOutput component for real-time log viewing ## Documentation - Protocol 13 detailed documentation - Dashboard visualization guide - Plotly components README - Updated run-optimization skill with Mode 5 (adaptive) ## Bug Fixes - Fixed TypeScript errors in dashboard components - Fixed Card component to accept ReactNode title - Removed unused imports across components 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-12-04 07:41:54 -05:00
6<0E><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F>zupkfa\WRMHC>94/*%  <0E><0E><0E> 6 5 4 3 2 1 0 / . - , + * ) ( ' & % $ # " !                           
          /V<0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F><0F>~ytoic]WQKE?93-'! <00> -<2D>zernike_coeffs_3[-122.99194234692816, -1.5340497189796296, 0.4862018057085856, 107.7288118618432, -0.1418787978946583, 22.29175025741636, 24.143835491475556, 0.5165046455360954, -13.174492661568475, -0.6834374340109284, -53.7547995064518, 0.4795929583078915, 16.422434119004972, 0.2062087193542832, 36.80067428418011, 9.165763996276613, -0.23623473392032493, 29.8307435201677, 0.07901307935577023, -4.217840511859243, -0.3532653485012642, 14.994800538973696, -0.0868335773186415, -14.200730510978005, 0.06462241277810331, 0.10747385577166127, 0.3250270786415393, 13.185063624611464, -1.0836370777555178, 0.439919788889982, -8.635169911117542, 0.12005626340178155, 14.927369351843506, 0.13824461220215256, -4.42396644594713, -0.16853817796155576, 2.8480430317716854, -0.08081752464305948, 7.74564122073199, -0.10556987299863145, -1.0597769467176321, 0.00870786640960973, -2.9582197576667135, 0.014276030715641005, 1.238337335211307, 0.9723322469727198, -0.09888299577817793, 3.1824458902472332, -0.051366975264900264, -8.49762851780092]<5D> -<2D>zernike_coeffs_2[-68.19958450111083, -3.1263227642997995, 0.38305017838790784, 88.92300069911208, 0.1593548714746233, 24.393122171392204, 29.70120374865821, 0.19647218171705902, -17.290342838401404, -0.7495692364274953, -54.06658518297985, 0.5442315618868326, 20.50202219973817, 0.21722628945664815, 45.64767560976245, 11.038792614237956, -0.10438158562561899, 36.67482248507444, 0.11954770107684554, -5.271846996543763, -0.36000973164881617, 11.637411276650028, -0.17225793978574805, -17.192810922236628, 0.10229452765200132, -0.19432820562376008, 0.3794733026938694, 12.097183483389248, -1.2203506783687745, 0.29705732872272894, -10.5600244489527, 0.12926131424040338, 18.307164424048935, 0.1155248902313639, -5.433261508846018, -0.11399765545111665, 0.9430642748902949, -0.032697975948544074, 9.418486961987668, -0.1715264318730679, -1.1950249098198205, -0.012071331517051144, 0.8771039450219847, -0.024917906660874487, 1.4923076399202055, 1.164087028977585, 0.009818529482719688, 3.87188298203594, -0.06281286985950382, -10.40504937390941]<5D>+ -<2D>7zernike_coeffs_1[-185.16535972348885, 2.787411912700243, 0.47788452399841236, 96.81564170585591, -0.7467180835866323, 6.611344651709473, -0.18884570837090917, 0.9790261609243909, 2.5295283371679447, -0.19887380992093576, -26.59382125873229, 0.09872361036842466, -0.7992811773029378, 0.08001871928926625, -1.13068594342546, 0.45850834681560027, -0.4152567306708753, -0.18340734315506232, -0.05067060491893756, 0.21923450222978086, -0.16425274541290397, 15.132759900690784, 0.14724074237931106, -0.5084562793496061, -0.05156574848374624, 0.7305233981054596, 0.04307597005280232, 9.130824506255857, -0.24397646791932612, 0.5433286682894566, -0.07304126985094321, 0.040337615958961286, 0.009003878661957801, 0.12107513963215966, 0.01443918682032241, -0.207725747784421, 5.712645431149719, -0.14880704392569086, 0.1858469590389766, 0.09412632537321178, -0.23514491751832456, 0.05096116087254263, -10.092049380549943, 0.09503209078738398, 0.05989646010947174, 0.06418796365947263, -0.2936680925608379, 0.07161939395407742, -0.000U<>LQ<>IP<>CO<>@N<>:M<>7L<>4K<>.H<>+G<>%F<>"E<>D<>A<>@<40>><3E> =<3D><<3C>;~:{9x8r7o6i5f4`3]0Z/T.Q-K,H+B*?)<(6'3&-
feat: Add M1 mirror Zernike optimization with correct RMS calculation Major improvements to telescope mirror optimization workflow: Assembly FEM Workflow (solve_simulation.py): - Fixed multi-part assembly FEM update sequence - Use ImportFromFile() for reliable expression updates - Add DuplicateNodesCheckBuilder with MergeOccurrenceNodes=True - Switch to Foreground solve mode for multi-subcase solutions - Add detailed logging and diagnostics for node merge operations Zernike RMS Calculation: - CRITICAL FIX: Use correct surface-based RMS formula - Global RMS = sqrt(mean(W^2)) from actual WFE values - Filtered RMS = sqrt(mean(W_residual^2)) after removing low-order fit - This matches zernike_Post_Script_NX.py (optical standard) - Previous WRONG formula was: sqrt(sum(coeffs^2)) - Add compute_rms_filter_j1to3() for optician workload metric Subcase Mapping: - Fix subcase mapping to match NX model: - Subcase 1 = 90 deg (polishing orientation) - Subcase 2 = 20 deg (reference) - Subcase 3 = 40 deg - Subcase 4 = 60 deg New Study: M1 Mirror Zernike Optimization - Full optimization config with 11 design variables - 3 objectives: rel_filtered_rms_40_vs_20, rel_filtered_rms_60_vs_20, mfg_90_optician_workload - Neural surrogate support for accelerated optimization Documentation: - Update ZERNIKE_INTEGRATION.md with correct RMS formula - Update ASSEMBLY_FEM_WORKFLOW.md with expression import and node merge details - Add reference scripts from original zernike_Post_Script_NX.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-28 16:30:15 -05:00
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feat: Add Protocol 13 adaptive optimization, Plotly charts, and dashboard improvements ## Protocol 13: Adaptive Multi-Objective Optimization - Iterative FEA + Neural Network surrogate workflow - Initial FEA sampling, NN training, NN-accelerated search - FEA validation of top NN predictions, retraining loop - adaptive_state.json tracks iteration history and best values - M1 mirror study (V11) with 103 FEA, 3000 NN trials ## Dashboard Visualization Enhancements - Added Plotly.js interactive charts (parallel coords, Pareto, convergence) - Lazy loading with React.lazy() for performance - Code splitting: plotly.js-basic-dist (~1MB vs 3.5MB) - Chart library toggle (Recharts default, Plotly on-demand) - ExpandableChart component for full-screen modal views - ConsoleOutput component for real-time log viewing ## Documentation - Protocol 13 detailed documentation - Dashboard visualization guide - Plotly components README - Updated run-optimization skill with Mode 5 (adaptive) ## Bug Fixes - Fixed TypeScript errors in dashboard components - Fixed Card component to accept ReactNode title - Removed unused imports across components 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-12-04 07:41:54 -05:00
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!solve_time !objectives -zernike_coeffs_4 -zernike_coeffs_3 -zernike_coeffs_2 -zernike_coeffs_1  source !solveI! objectivesX 

feat: Add M1 mirror Zernike optimization with correct RMS calculation Major improvements to telescope mirror optimization workflow: Assembly FEM Workflow (solve_simulation.py): - Fixed multi-part assembly FEM update sequence - Use ImportFromFile() for reliable expression updates - Add DuplicateNodesCheckBuilder with MergeOccurrenceNodes=True - Switch to Foreground solve mode for multi-subcase solutions - Add detailed logging and diagnostics for node merge operations Zernike RMS Calculation: - CRITICAL FIX: Use correct surface-based RMS formula - Global RMS = sqrt(mean(W^2)) from actual WFE values - Filtered RMS = sqrt(mean(W_residual^2)) after removing low-order fit - This matches zernike_Post_Script_NX.py (optical standard) - Previous WRONG formula was: sqrt(sum(coeffs^2)) - Add compute_rms_filter_j1to3() for optician workload metric Subcase Mapping: - Fix subcase mapping to match NX model: - Subcase 1 = 90 deg (polishing orientation) - Subcase 2 = 20 deg (reference) - Subcase 3 = 40 deg - Subcase 4 = 60 deg New Study: M1 Mirror Zernike Optimization - Full optimization config with 11 design variables - 3 objectives: rel_filtered_rms_40_vs_20, rel_filtered_rms_60_vs_20, mfg_90_optician_workload - Neural surrogate support for accelerated optimization Documentation: - Update ZERNIKE_INTEGRATION.md with correct RMS formula - Update ASSEMBLY_FEM_WORKFLOW.md with expression import and node merge details - Add reference scripts from original zernike_Post_Script_NX.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-28 16:30:15 -05:00
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feat: Add Protocol 13 adaptive optimization, Plotly charts, and dashboard improvements ## Protocol 13: Adaptive Multi-Objective Optimization - Iterative FEA + Neural Network surrogate workflow - Initial FEA sampling, NN training, NN-accelerated search - FEA validation of top NN predictions, retraining loop - adaptive_state.json tracks iteration history and best values - M1 mirror study (V11) with 103 FEA, 3000 NN trials ## Dashboard Visualization Enhancements - Added Plotly.js interactive charts (parallel coords, Pareto, convergence) - Lazy loading with React.lazy() for performance - Code splitting: plotly.js-basic-dist (~1MB vs 3.5MB) - Chart library toggle (Recharts default, Plotly on-demand) - ExpandableChart component for full-screen modal views - ConsoleOutput component for real-time log viewing ## Documentation - Protocol 13 detailed documentation - Dashboard visualization guide - Plotly components README - Updated run-optimization skill with Mode 5 (adaptive) ## Bug Fixes - Fixed TypeScript errors in dashboard components - Fixed Card component to accept ReactNode title - Removed unused imports across components 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-12-04 07:41:54 -05:00
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feat: Add M1 mirror Zernike optimization with correct RMS calculation Major improvements to telescope mirror optimization workflow: Assembly FEM Workflow (solve_simulation.py): - Fixed multi-part assembly FEM update sequence - Use ImportFromFile() for reliable expression updates - Add DuplicateNodesCheckBuilder with MergeOccurrenceNodes=True - Switch to Foreground solve mode for multi-subcase solutions - Add detailed logging and diagnostics for node merge operations Zernike RMS Calculation: - CRITICAL FIX: Use correct surface-based RMS formula - Global RMS = sqrt(mean(W^2)) from actual WFE values - Filtered RMS = sqrt(mean(W_residual^2)) after removing low-order fit - This matches zernike_Post_Script_NX.py (optical standard) - Previous WRONG formula was: sqrt(sum(coeffs^2)) - Add compute_rms_filter_j1to3() for optician workload metric Subcase Mapping: - Fix subcase mapping to match NX model: - Subcase 1 = 90 deg (polishing orientation) - Subcase 2 = 20 deg (reference) - Subcase 3 = 40 deg - Subcase 4 = 60 deg New Study: M1 Mirror Zernike Optimization - Full optimization config with 11 design variables - 3 objectives: rel_filtered_rms_40_vs_20, rel_filtered_rms_60_vs_20, mfg_90_optician_workload - Neural surrogate support for accelerated optimization Documentation: - Update ZERNIKE_INTEGRATION.md with correct RMS formula - Update ASSEMBLY_FEM_WORKFLOW.md with expression import and node merge details - Add reference scripts from original zernike_Post_Script_NX.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-28 16:30:15 -05:00
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feat: Add Protocol 13 adaptive optimization, Plotly charts, and dashboard improvements ## Protocol 13: Adaptive Multi-Objective Optimization - Iterative FEA + Neural Network surrogate workflow - Initial FEA sampling, NN training, NN-accelerated search - FEA validation of top NN predictions, retraining loop - adaptive_state.json tracks iteration history and best values - M1 mirror study (V11) with 103 FEA, 3000 NN trials ## Dashboard Visualization Enhancements - Added Plotly.js interactive charts (parallel coords, Pareto, convergence) - Lazy loading with React.lazy() for performance - Code splitting: plotly.js-basic-dist (~1MB vs 3.5MB) - Chart library toggle (Recharts default, Plotly on-demand) - ExpandableChart component for full-screen modal views - ConsoleOutput component for real-time log viewing ## Documentation - Protocol 13 detailed documentation - Dashboard visualization guide - Plotly components README - Updated run-optimization skill with Mode 5 (adaptive) ## Bug Fixes - Fixed TypeScript errors in dashboard components - Fixed Card component to accept ReactNode title - Removed unused imports across components 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-12-04 07:41:54 -05:00
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feat: Add M1 mirror Zernike optimization with correct RMS calculation Major improvements to telescope mirror optimization workflow: Assembly FEM Workflow (solve_simulation.py): - Fixed multi-part assembly FEM update sequence - Use ImportFromFile() for reliable expression updates - Add DuplicateNodesCheckBuilder with MergeOccurrenceNodes=True - Switch to Foreground solve mode for multi-subcase solutions - Add detailed logging and diagnostics for node merge operations Zernike RMS Calculation: - CRITICAL FIX: Use correct surface-based RMS formula - Global RMS = sqrt(mean(W^2)) from actual WFE values - Filtered RMS = sqrt(mean(W_residual^2)) after removing low-order fit - This matches zernike_Post_Script_NX.py (optical standard) - Previous WRONG formula was: sqrt(sum(coeffs^2)) - Add compute_rms_filter_j1to3() for optician workload metric Subcase Mapping: - Fix subcase mapping to match NX model: - Subcase 1 = 90 deg (polishing orientation) - Subcase 2 = 20 deg (reference) - Subcase 3 = 40 deg - Subcase 4 = 60 deg New Study: M1 Mirror Zernike Optimization - Full optimization config with 11 design variables - 3 objectives: rel_filtered_rms_40_vs_20, rel_filtered_rms_60_vs_20, mfg_90_optician_workload - Neural surrogate support for accelerated optimization Documentation: - Update ZERNIKE_INTEGRATION.md with correct RMS formula - Update ASSEMBLY_FEM_WORKFLOW.md with expression import and node merge details - Add reference scripts from original zernike_Post_Script_NX.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-28 16:30:15 -05:00
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feat: Add Protocol 13 adaptive optimization, Plotly charts, and dashboard improvements ## Protocol 13: Adaptive Multi-Objective Optimization - Iterative FEA + Neural Network surrogate workflow - Initial FEA sampling, NN training, NN-accelerated search - FEA validation of top NN predictions, retraining loop - adaptive_state.json tracks iteration history and best values - M1 mirror study (V11) with 103 FEA, 3000 NN trials ## Dashboard Visualization Enhancements - Added Plotly.js interactive charts (parallel coords, Pareto, convergence) - Lazy loading with React.lazy() for performance - Code splitting: plotly.js-basic-dist (~1MB vs 3.5MB) - Chart library toggle (Recharts default, Plotly on-demand) - ExpandableChart component for full-screen modal views - ConsoleOutput component for real-time log viewing ## Documentation - Protocol 13 detailed documentation - Dashboard visualization guide - Plotly components README - Updated run-optimization skill with Mode 5 (adaptive) ## Bug Fixes - Fixed TypeScript errors in dashboard components - Fixed Card component to accept ReactNode title - Removed unused imports across components 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-12-04 07:41:54 -05:00
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feat: Add M1 mirror Zernike optimization with correct RMS calculation Major improvements to telescope mirror optimization workflow: Assembly FEM Workflow (solve_simulation.py): - Fixed multi-part assembly FEM update sequence - Use ImportFromFile() for reliable expression updates - Add DuplicateNodesCheckBuilder with MergeOccurrenceNodes=True - Switch to Foreground solve mode for multi-subcase solutions - Add detailed logging and diagnostics for node merge operations Zernike RMS Calculation: - CRITICAL FIX: Use correct surface-based RMS formula - Global RMS = sqrt(mean(W^2)) from actual WFE values - Filtered RMS = sqrt(mean(W_residual^2)) after removing low-order fit - This matches zernike_Post_Script_NX.py (optical standard) - Previous WRONG formula was: sqrt(sum(coeffs^2)) - Add compute_rms_filter_j1to3() for optician workload metric Subcase Mapping: - Fix subcase mapping to match NX model: - Subcase 1 = 90 deg (polishing orientation) - Subcase 2 = 20 deg (reference) - Subcase 3 = 40 deg - Subcase 4 = 60 deg New Study: M1 Mirror Zernike Optimization - Full optimization config with 11 design variables - 3 objectives: rel_filtered_rms_40_vs_20, rel_filtered_rms_60_vs_20, mfg_90_optician_workload - Neural surrogate support for accelerated optimization Documentation: - Update ZERNIKE_INTEGRATION.md with correct RMS formula - Update ASSEMBLY_FEM_WORKFLOW.md with expression import and node merge details - Add reference scripts from original zernike_Post_Script_NX.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-28 16:30:15 -05:00
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feat: Add Protocol 13 adaptive optimization, Plotly charts, and dashboard improvements ## Protocol 13: Adaptive Multi-Objective Optimization - Iterative FEA + Neural Network surrogate workflow - Initial FEA sampling, NN training, NN-accelerated search - FEA validation of top NN predictions, retraining loop - adaptive_state.json tracks iteration history and best values - M1 mirror study (V11) with 103 FEA, 3000 NN trials ## Dashboard Visualization Enhancements - Added Plotly.js interactive charts (parallel coords, Pareto, convergence) - Lazy loading with React.lazy() for performance - Code splitting: plotly.js-basic-dist (~1MB vs 3.5MB) - Chart library toggle (Recharts default, Plotly on-demand) - ExpandableChart component for full-screen modal views - ConsoleOutput component for real-time log viewing ## Documentation - Protocol 13 detailed documentation - Dashboard visualization guide - Plotly components README - Updated run-optimization skill with Mode 5 (adaptive) ## Bug Fixes - Fixed TypeScript errors in dashboard components - Fixed Card component to accept ReactNode title - Removed unused imports across components 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-12-04 07:41:54 -05:00
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feat: Add M1 mirror Zernike optimization with correct RMS calculation Major improvements to telescope mirror optimization workflow: Assembly FEM Workflow (solve_simulation.py): - Fixed multi-part assembly FEM update sequence - Use ImportFromFile() for reliable expression updates - Add DuplicateNodesCheckBuilder with MergeOccurrenceNodes=True - Switch to Foreground solve mode for multi-subcase solutions - Add detailed logging and diagnostics for node merge operations Zernike RMS Calculation: - CRITICAL FIX: Use correct surface-based RMS formula - Global RMS = sqrt(mean(W^2)) from actual WFE values - Filtered RMS = sqrt(mean(W_residual^2)) after removing low-order fit - This matches zernike_Post_Script_NX.py (optical standard) - Previous WRONG formula was: sqrt(sum(coeffs^2)) - Add compute_rms_filter_j1to3() for optician workload metric Subcase Mapping: - Fix subcase mapping to match NX model: - Subcase 1 = 90 deg (polishing orientation) - Subcase 2 = 20 deg (reference) - Subcase 3 = 40 deg - Subcase 4 = 60 deg New Study: M1 Mirror Zernike Optimization - Full optimization config with 11 design variables - 3 objectives: rel_filtered_rms_40_vs_20, rel_filtered_rms_60_vs_20, mfg_90_optician_workload - Neural surrogate support for accelerated optimization Documentation: - Update ZERNIKE_INTEGRATION.md with correct RMS formula - Update ASSEMBLY_FEM_WORKFLOW.md with expression import and node merge details - Add reference scripts from original zernike_Post_Script_NX.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-28 16:30:15 -05:00
   

<EFBFBD>K<07>d<07><04>a<04><01>(<01><01>Qe?indexsqlite_autoindex_trial_intermediate_values_1trial_intermediate_values<16>E%%<01>Mtabletrial_valuestrial_valuesCREATE TABLE trial_values (
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)<04>K%indexsqlite_autoindex_trial_values_1trial_values<14>,%%<01>tabletrial_paramstrial_paramsCREATE TABLE trial_params (
param_id INTEGER NOT NULL,
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trial_system_attribute_id INTEGER NOT NULL,
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)<01>a;indexsqlite_autoindex_trial_system_attributes_1trial_system_attributes<10>? 77<01>tabletrial_user_attributestrial_user_attributes CREATE TABLE trial_user_attributes (
trial_user_attribute_id INTEGER NOT NULL,
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<EFBFBD>@77<01>tablestudy_user_attributesstudy_user_attributesCREATE TABLE study_user_attributes (
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study_id INTEGER,
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UNIQUE (study_id, "key"),
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)I]7indexsqlite_autoindex_study_user_attributes_1study_user_attributes<08>J--<01>Gtablestudy_directionsstudy_directionsCREATE TABLE study_directions (
study_direction_id INTEGER NOT NULL,
direction VARCHAR(8) NOT NULL,
study_id INTEGER NOT NULL,
objective INTEGER NOT NULL,
PRIMARY KEY (study_direction_id),
UNIQUE (study_id, objective),
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)?S-indexsqlite_autoindex_study_directions_1study_directions<00>\%%<01>{tableversion_infoversion_infoCREATE TABLE version_info (
version_info_id INTEGER NOT NULL,
schema_version INTEGER,
library_version VARCHAR(256),
PRIMARY KEY (version_info_id),
CHECK (version_info_id=1)
)j7<01>indexix_studies_study_namestudiesCREATE UNIQUE INDEX ix_studies_study_name ON studies (study_name)<29> <01>otablestudiesstudiesCREATE TABLE studies (
study_id INTEGER NOT NULL,
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) <07><05><0E>s ' <0C> <0B> p
( <09>V<06><07><05>Z<00>)++<01> tablealembic_versionalembic_versionCREATE TABLE alembic_version (
version_num VARCHAR(32) NOT NULL,
CONSTRAINT alembic_version_pkc PRIMARY KEY (version_num)
)=Q+indexsqlite_autoindex_alembic_version_1alembic_version<1C>--<01>otabletrial_heartbeatstrial_heartbeatsCREATE TABLE trial_heartbeats (
trial_heartbeat_id INTEGER NOT NULL,
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heartbeat DATETIME NOT NULL,
PRIMARY KEY (trial_heartbeat_id),
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)I ]7indexsqlite_autoindex_trial_user_attributes_1trial_user_attributes<0E>? 77<01>tabletrial_user_attributestrial_user_attributes CREATE TABLE trial_user_attributes (
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v3.2.0.a
<0F><0F>  v3.2.0.a
<EFBFBD><06><02><00> -<2D>zernike_coeffs_3[-122.99194234692816, -1.5340497189796296, 0.4862018057085856, 107.7288118618432, -0.1418787978946583, 22.29175025741636, 24.143835491475556, 0.5165046455360954, -13.174492661568475, -0.6834374340109284, -53.7547995064518, 0.4795929583078915, 16.422434119004972, 0.2062087193542832, 36.80067428418011, 9.165763996276613, -0.23623473392032493, 29.8307435201677, 0.07901307935577023, -4.217840511859243, -0.3532653485012642, 14.994800538973696, -0.0868335773186415, -14.200730510978005, 0.06462241277810331, 0.10747385577166127, 0.3250270786415393, 13.185063624611464, -1.0836370777555178, 0.439919788889982, -8.635169911117542, 0.12005626340178155, 14.927369351843506, 0.13824461220215256, -4.42396644594713, -0.16853817796155576, 2.8480430317716854, -0.08081752464305948, 7.74564122073199, -0.10556987299863145, -1.0597769467176321, 0.00870786640960973, -2.9582197576667135, 0.014276030715641005, 1.238337335211307, 0.9723322469727198, -0.09888299577817793, 3.1824458902472332, -0.051366975264900264, -8.49762851780092]<5D> -<2D>zernike_coeffs_2[-68.19958450111083, -3.1263227642997995, 0.38305017838790784, 88.92300069911208, 0.1593548714746233, 24.393122171392204, 29.70120374865821, 0.19647218171705902, -17.290342838401404, -0.7495692364274953, -54.06658518297985, 0.5442315618868326, 20.50202219973817, 0.21722628945664815, 45.64767560976245, 11.038792614237956, -0.10438158562561899, 36.67482248507444, 0.11954770107684554, -5.271846996543763, -0.36000973164881617, 11.637411276650028, -0.17225793978574805, -17.192810922236628, 0.10229452765200132, -0.19432820562376008, 0.3794733026938694, 12.097183483389248, -1.2203506783687745, 0.29705732872272894, -10.5600244489527, 0.12926131424040338, 18.307164424048935, 0.1155248902313639, -5.433261508846018, -0.11399765545111665, 0.9430642748902949, -0.032697975948544074, 9.418486961987668, -0.1715264318730679, -1.1950249098198205, -0.012071331517051144, 0.8771039450219847, -0.024917906660874487, 1.4923076399202055, 1.164087028977585, 0.009818529482719688, 3.87188298203594, -0.06281286985950382, -10.40504937390941]<5D>+ -<2D>7zernike_coeffs_1[-185.16535972348885, 2.787411912700243, 0.47788452399841236, 96.81564170585591, -0.7467180835866323, 6.611344651709473, -0.18884570837090917, 0.9790261609243909, 2.5295283371679447, -0.19887380992093576, -26.59382125873229, 0.09872361036842466, -0.7992811773029378, 0.08001871928926625, -1.13068594342546, 0.45850834681560027, -0.4152567306708753, -0.18340734315506232, -0.05067060491893756, 0.21923450222978086, -0.16425274541290397, 15.132759900690784, 0.14724074237931106, -0.5084562793496061, -0.05156574848374624, 0.7305233981054596, 0.04307597005280232, 9.130824506255857, -0.24397646791932612, 0.5433286682894566, -0.07304126985094321, 0.040337615958961286, 0.009003878661957801, 0.12107513963215966, 0.01443918682032241, -0.207725747784421, 5.712645431149719, -0.14880704392569086, 0.1858469590389766, 0.09412632537321178, -0.23514491751832456, 0.05096116087254263, -10.092049380549943, 0.09503209078738398, 0.05989646010947174, 0.06418796365947263, -0.2936680925608379, 0.07161939395407742, -0.000444110638235759, -0.04225624305714225] source"FEA"  !/solve_time48.88705110549927<EFBFBD>  !<21>-objectives{"rel_filtered_rms_40_vs_20": 87.36360083902647, "rel_filtered_rms_60_vs_20": 72.63776565818178, "mfg_90_optician_workload": 51.256518916490315} <02> <0B> F #
source"FEA"! !/solve_time49.82278513908386<EFBFBD> !<21>+objectives{"rel_filtered_rms_40_vs_20": 87.36360083902622, "rel_filtered_rms_60_vs_20": 72.63776565818213, "mfg_90_optician_workload": 51.25651891649045}<7D> -<2D> zernike_coeffs_4[-162.94967905262976, 0.24322736354125643, 0.5307115756854942, 113.5412745877274, -0.42599724257851773, 17.5017990823644, 15.674545770771308, 0.7742382504493786, -7.469714940637611, -0.5348772042212031, -46.95966483292862, 0.3571117789251769, 10.362184626291182, 0.17032028000606045, 23.51524925174206, 6.187275223899509, -0.33959443215312235, 19.38886101228934, 0.028949457938712286, -2.6551328510576786, -0.303913970028578, 16.543634383994757, 0.009062542121327996, -9.495935516894752, 0.019156842062947554, 0.39631028048113265, 0.23138008389892215, 12.682684693974526, -0.8162275494412601, 0.529722386818336, -5.668850450873805, 0.09637137837682436, 9.74722495818131, 0.14429051255406525, -2.881108923391754, -0.20275058789436903, 4.409499104605545, -0.11918903468022758, 5.138613425241683, -0.02688117784651236, -0.7967106450118797, 0.028436748450412788, -6.436711908490754, 0.05174762065389135, 0.8350144131901447, 0.6633067215363802, -0.19565783274801937, 2.109182172630534, -0.03372597866521548, -5.565331665746165] <03> <0B><07><03><00>5<>zernike_rel_coeffs_3[62.17341737656053, -4.321461631679949, 0.008317281710171948, 10.913170155988036, 0.6048392856917759, 15.680405605706934, 24.332681199846512, -0.4625215153880527, -15.704020998736345, -0.48456362408993303, -27.160978247720212, 0.38086934793911464, 17.221715296307877, 0.12619000006492279, 37.93136022760583, 8.707255649460926, 0.17902199675038064, 30.014150863322648, 0.1296836842747816, -4.437075014088842, -0.1890126030882572, -0.13795936171642356, -0.23407431969799625, -13.692274231628408, 0.11618816126178991, -0.6230495423338579, 0.2819511085888848, 4.054239118355501, -0.8396606098360945, -0.10340887939934819, -8.562128641266614, 0.07971864744287742, 14.918365473181403, 0.01716947257005467, -4.438405632767358, 0.03918756982290782, -2.864602399378874, 0.06798951928246867, 7.559794261693126, -0.1996961983718944, -0.824632029199399, -0.04225329446283785, 7.133829622883274, -0.08075606007170834, 1.1784408751018876, 0.9081442833130854, 0.1947850967828179, 3.110826496293058, -0.05092286462647579, -8.455372274743965]<5D>-<2D>zernike_coeffs_3[-122.99194234692816, -1.5340497189796296, 0.4862018057085856, 107.7288118618432, -0.1418787978946583, 22.29175025741636, 24.143835491475556, 0.5165046455360954, -13.174492661568475, -0.6834374340109284, -53.7547995064518, 0.4795929583078915, 16.422434119004972, 0.2062087193542832, 36.80067428418011, 9.165763996276613, -0.23623473392032493, 29.8307435201677, 0.07901307935577023, -4.217840511859243, -0.3532653485012642, 14.994800538973696, -0.0868335773186415, -14.200730510978005, 0.06462241277810331, 0.10747385577166127, 0.3250270786415393, 13.185063624611464, -1.0836370777555178, 0.439919788889982, -8.635169911117542, 0.12005626340178155, 14.927369351843506, 0.13824461220215256, -4.42396644594713, -0.16853817796155576, 2.8480430317716854, -0.08081752464305948, 7.74564122073199, -0.105569872998631
<EFBFBD><06><02><00>*-<2D>zernike_coeffs_2[-68.19958450111083, -3.1263227642997995, 0.38305017838790784, 88.92300069911208, 0.1593548714746233, 24.393122171392204, 29.70120374865821, 0.19647218171705902, -17.290342838401404, -0.7495692364274953, -54.06658518297985, 0.5442315618868326, 20.50202219973817, 0.21722628945664815, 45.64767560976245, 11.038792614237956, -0.10438158562561899, 36.67482248507444, 0.11954770107684554, -5.271846996543763, -0.36000973164881617, 11.637411276650028, -0.17225793978574805, -17.192810922236628, 0.10229452765200132, -0.19432820562376008, 0.3794733026938694, 12.097183483389248, -1.2203506783687745, 0.29705732872272894, -10.5600244489527, 0.12926131424040338, 18.307164424048935, 0.1155248902313639, -5.433261508846018, -0.11399765545111665, 0.9430642748902949, -0.032697975948544074, 9.418486961987668, -0.1715264318730679, -1.1950249098198205, -0.012071331517051144, 0.8771039450219847, -0.024917906660874487, 1.4923076399202055, 1.164087028977585, 0.009818529482719688, 3.87188298203594, -0.06281286985950382, -10.40504937390941]<5D>,)-<2D>7zernike_coeffs_1[-185.16535972348885, 2.787411912700243, 0.47788452399841236, 96.81564170585591, -0.7467180835866323, 6.611344651709473, -0.18884570837090917, 0.9790261609243909, 2.5295283371679447, -0.19887380992093576, -26.59382125873229, 0.09872361036842466, -0.7992811773029378, 0.08001871928926625, -1.13068594342546, 0.45850834681560027, -0.4152567306708753, -0.18340734315506232, -0.05067060491893756, 0.21923450222978086, -0.16425274541290397, 15.132759900690784, 0.14724074237931106, -0.5084562793496061, -0.05156574848374624, 0.7305233981054596, 0.04307597005280232, 9.130824506255857, -0.24397646791932612, 0.5433286682894566, -0.07304126985094321, 0.040337615958961286, 0.009003878661957801, 0.12107513963215966, 0.01443918682032241, -0.207725747784421, 5.712645431149719, -0.14880704392569086, 0.1858469590389766, 0.09412632537321178, -0.23514491751832456, 0.05096116087254263, -10.092049380549943, 0.09503209078738398, 0.05989646010947174, 0.06418796365947263, -0.2936680925608379, 0.07161939395407742, -0.000444110638235759, -0.04225624305714225](source"FEA"!'!/solve_time50.99253964424133<EFBFBD> &!<21>+objectives{"rel_filtered_rms_40_vs_20": 87.36360083902622, "rel_filtered_rms_60_vs_20": 72.63776565818213, "mfg_90_optician_workload": 51.25651891649045}<7D>+%5<>-zernike_rel_coeffs_4[22.215680670859037, -2.5441845491592012, 0.052827051686958705, 16.725632881871263, 0.3207208410079074, 10.890454430654883, 15.863391479142509, -0.20478791047497796, -9.999243277805443, -0.3360033943001795, -20.365843574196607, 0.25838816855652735, 11.161465803594227, 0.09030156071670328, 24.645935195167503, 5.728766877083747, 0.07566229851765945, 19.572268355444482, 0.07962006285776604, -2.8743673532873824, -0.13966122461551553, 1.4108744833045193, -0.1381782002582733, -8.987479237545188, 0.07072259054669779, -0.33421311762439065, 0.1883041138463822, 3.5518601877185825, -0.5722510815216975, -0.01360628147103752, -5.595809181022862, 0.056033762417798234, 9.738221079519274, 0.023215372921985996, -2.8955481102119336, 0.004975159890063208, -1.3031463265441763, 0.029618009245491912, 4.952766466202811, -0.12100750321974953, -0.5615657274935687, -0.022524412422066716, 3.6553374720592173, -0.043284470133472946, 0.7751179530806263, 0.5991187578766961, 0.09801025981291746, 2.0375627786763433, -0.03328186802681316, -5.523075422689088] <03> <0B><07><03>
<EFBFBD><06><02><00>H-<2D>zernike_coeffs_2[-68.19958450111083, -3.1263227642997995, 0.38305017838790784, 88.92300069911208, 0.1593548714746233, 24.393122171392204, 29.70120374865821, 0.19647218171705902, -17.290342838401404, -0.7495692364274953, -54.06658518297985, 0.5442315618868326, 20.50202219973817, 0.21722628945664815, 45.64767560976245, 11.038792614237956, -0.10438158562561899, 36.67482248507444, 0.11954770107684554, -5.271846996543763, -0.36000973164881617, 11.637411276650028, -0.17225793978574805, -17.192810922236628, 0.10229452765200132, -0.19432820562376008, 0.3794733026938694, 12.097183483389248, -1.2203506783687745, 0.29705732872272894, -10.5600244489527, 0.12926131424040338, 18.307164424048935, 0.1155248902313639, -5.433261508846018, -0.11399765545111665, 0.9430642748902949, -0.032697975948544074, 9.418486961987668, -0.1715264318730679, -1.1950249098198205, -0.012071331517051144, 0.8771039450219847, -0.024917906660874487, 1.4923076399202055, 1.164087028977585, 0.009818529482719688, 3.87188298203594, -0.06281286985950382, -10.40504937390941]<5D>,G-<2D>7zernike_coeffs_1[-185.16535972348885, 2.787411912700243, 0.47788452399841236, 96.81564170585591, -0.7467180835866323, 6.611344651709473, -0.18884570837090917, 0.9790261609243909, 2.5295283371679447, -0.19887380992093576, -26.59382125873229, 0.09872361036842466, -0.7992811773029378, 0.08001871928926625, -1.13068594342546, 0.45850834681560027, -0.4152567306708753, -0.18340734315506232, -0.05067060491893756, 0.21923450222978086, -0.16425274541290397, 15.132759900690784, 0.14724074237931106, -0.5084562793496061, -0.05156574848374624, 0.7305233981054596, 0.04307597005280232, 9.130824506255857, -0.24397646791932612, 0.5433286682894566, -0.07304126985094321, 0.040337615958961286, 0.009003878661957801, 0.12107513963215966, 0.01443918682032241, -0.207725747784421, 5.712645431149719, -0.14880704392569086, 0.1858469590389766, 0.09412632537321178, -0.23514491751832456, 0.05096116087254263, -10.092049380549943, 0.09503209078738398, 0.05989646010947174, 0.06418796365947263, -0.2936680925608379, 0.07161939395407742, -0.000444110638235759, -0.04225624305714225]Fsource"FEA" E!-solve_time49.1385600566864<EFBFBD> D!<21>+objectives{"rel_filtered_rms_40_vs_20": 87.36360083902622, "rel_filtered_rms_60_vs_20": 72.63776565818213, "mfg_90_optician_workload": 51.25651891649045}<7D>+C5<>-zernike_rel_coeffs_4[22.215680670859037, -2.5441845491592012, 0.052827051686958705, 16.725632881871263, 0.3207208410079074, 10.890454430654883, 15.863391479142509, -0.20478791047497796, -9.999243277805443, -0.3360033943001795, -20.365843574196607, 0.25838816855652735, 11.161465803594227, 0.09030156071670328, 24.645935195167503, 5.728766877083747, 0.07566229851765945, 19.572268355444482, 0.07962006285776604, -2.8743673532873824, -0.13966122461551553, 1.4108744833045193, -0.1381782002582733, -8.987479237545188, 0.07072259054669779, -0.33421311762439065, 0.1883041138463822, 3.5518601877185825, -0.5722510815216975, -0.01360628147103752, -5.595809181022862, 0.056033762417798234, 9.738221079519274, 0.023215372921985996, -2.8955481102119336, 0.004975159890063208, -1.3031463265441763, 0.029618009245491912, 4.952766466202811, -0.12100750321974953, -0.5615657274935687, -0.022524412422066716, 3.6553374720592173, -0.043284470133472946, 0.7751179530806263, 0.5991187578766961, 0.09801025981291746, 2.0375627786763433, -0.03328186802681316, -5.523075422689088] <03> <0B><07><03>
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inner_circular_rib_dia@<40>3<EFBFBD>M<EFBFBD>( {"name": "FloatDistribution", "attributes": {"step": null, "low": 480.0, "high": 620.0, "log": false}} <03> <0B><07><03><00>]5<> zernike_rel_coeffs_2[116.96577522237806, -5.9137346770001455, -0.09483434561062207, -7.8926410067439, 0.9060729550613635, 17.781777519682777, 29.890049457029008, -0.7825539792071298, -19.81987117556923, -0.5506954265064321, -27.47276392424736, 0.4455079515180844, 21.30130337704102, 0.13720757016728782, 46.77836155318775, 10.580284267422206, 0.3108751450452628, 36.85822982822953, 0.1702183059958379, -5.49108149877355, -0.1957569862356887, -3.4953486240410085, -0.31949868216532457, -16.68435464288708, 0.1538602761355331, -0.9248516037292158, 0.3363973326409547, 2.9663589771332988, -0.976374210449353, -0.24627133956678146, -10.486983179101752, 0.08892369828138957, 18.298160545386786, -0.005550249400743857, -5.447700695666163, 0.09372809233324705, -4.769581156259346, 0.11610906797705667, 9.232640002948703, -0.26565275724631526, -0.9598799923015788, -0.06303249238958211, 10.969153325572021, -0.1199499974482581, 1.4324111798106578, 1.0998990653180913, 0.30348662204356935, 3.800263588081778, -0.06236875922096119, -10.362793130852369]<5D>\-<2D> zernike_coeffs_2[-68.19958450111083, -3.1263227642997995, 0.38305017838790784, 88.92300069911208, 0.1593548714746233, 24.393122171392204, 29.70120374865821, 0.19647218171705902, -17.290342838401404, -0.7495692364274953, -54.06658518297985, 0.5442315618868326, 20.50202219973817, 0.21722628945664815, 45.64767560976245, 11.038792614237956, -0.10438158562561899, 36.67482248507444, 0.11954770107684554, -5.271846996543763, -0.36000973164881617, 11.637411276650028, -0.17225793978574805, -17.192810922236628, 0.10229452765200132, -0.19432820562376008, 0.3794733026938694, 12.097183483389248, -1.2203506783687745, 0.29705732872272894, -10.5600244489527, 0.12926131424040338, 18.307164424048935, 0.1155248902313639, -5.433261508846018, -0.11399765545111665, 0.9430642748902949, -0.032697975948544074, 9.418486961987668, -0.1715264318730679, -1.1950249098198205, -0.012071331517051144, 0.8771039450219847, -0.024917906660874487, 1.4923076399202055, 1.164087028977585, 0.009818529482719688, 3.87188298203594, -0.06281286985950382, -10.40504937390941]<5D>,[-<2D>7 zernike_coeffs_1[-185.16535972348885, 2.787411912700243, 0.47788452399841236, 96.81564170585591, -0.7467180835866323, 6.611344651709473, -0.18884570837090917, 0.9790261609243909, 2.5295283371679447, -0.19887380992093576, -26.59382125873229, 0.09872361036842466, -0.7992811773029378, 0.08001871928926625, -1.13068594342546, 0.45850834681560027, -0.4152567306708753, -0.18340734315506232, -0.05067060491893756, 0.21923450222978086, -0.16425274541290397, 15.132759900690784, 0.14724074237931106, -0.5084562793496061, -0.05156574848374624, 0.7305233981054596, 0.04307597005280232, 9.130824506255857, -0.24397646791932612, 0.5433286682894566, -0.07304126985094321, 0.040337615958961286, 0.009003878661957801, 0.12107513963215966, 0.01443918682032241, -0.207725747784421, 5.712645431149719, -0.14880704392569086, 0.1858469590389766, 0.09412632537321178, -0.23514491751832456, 0.05096116087254263, -10.092049380549943, 0.09503209078738
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feat: Add Protocol 13 adaptive optimization, Plotly charts, and dashboard improvements ## Protocol 13: Adaptive Multi-Objective Optimization - Iterative FEA + Neural Network surrogate workflow - Initial FEA sampling, NN training, NN-accelerated search - FEA validation of top NN predictions, retraining loop - adaptive_state.json tracks iteration history and best values - M1 mirror study (V11) with 103 FEA, 3000 NN trials ## Dashboard Visualization Enhancements - Added Plotly.js interactive charts (parallel coords, Pareto, convergence) - Lazy loading with React.lazy() for performance - Code splitting: plotly.js-basic-dist (~1MB vs 3.5MB) - Chart library toggle (Recharts default, Plotly on-demand) - ExpandableChart component for full-screen modal views - ConsoleOutput component for real-time log viewing ## Documentation - Protocol 13 detailed documentation - Dashboard visualization guide - Plotly components README - Updated run-optimization skill with Mode 5 (adaptive) ## Bug Fixes - Fixed TypeScript errors in dashboard components - Fixed Card component to accept ReactNode title - Removed unused imports across components 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-12-04 07:41:54 -05:00
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<EFBFBD><06><02><00>)<29>"5<>)*zernike_rel_coeffs_1[-115.79901303211241, 6.834308352846782, -0.13695051126471441, 1.2720885844907543, -0.8119072817847099, -16.328270769665366, -30.03714004886829, 0.6274432864823288, 20.748069551347108, 0.6009313516967358, 28.792539305241604, -0.3903858976093261, -22.154386785075573, -0.16750048551556715, -46.741251536058535, -10.790865708690662, -0.11228342268130355, -36.661737856308456, -0.34720666844029413, 6.023044044896896, 0.20319671927150507, 3.5337042997869403, 0.25187676010082705, 17.00352841313308, -0.0018122449252025263, 0.6629359234824731, -0.20428688365620967, -3.094429516365253, 0.8406886077596799, 0.05522993804451602, 10.29103346401668, 0.019886604107618586, -18.302647692667055, -0.12449227327472046, 5.79631333690714, 0.026761532469540675, 4.568105332794996, -0.27277354935847226, -9.204040188119963, 0.005984990254282542, 1.0492544831091706, 0.005302750154602233, -10.996291802002043, 0.1188584267509013, -1.1071677976938608, -1.0084699785306688, -0.11013491232911791, -3.7271124750752582, 0.06761790131700783, 10.224810785568904]<5D>4<EFBFBD>!-<2D>G*zernike_coeffs_1[-183.3020723334219, 3.1815694748941534, -0.16451555202499996, 86.86289937490112, -1.1983640765296113, 7.2098643836897285, -0.2596177910828995, 0.7869929669982119, 3.159853143852119, -0.11431667413719887, -24.216703949921982, 0.14126948934238154, -1.317979215114452, -0.03709037605509412, -0.3741028110116758, 0.00014299003457264933, -0.23625113140046602, -0.21120243522974905, -0.12371882675628693, 0.4197724118006831, -0.08439815011993146, 14.990746472360454, 0.05023042364760003, -0.21898587009452447, 0.07230987970872553, 0.4470789187525308, -0.06066234947528751, 8.803550392835003, -0.1696119364915314, 0.2806114388279783, -0.18374993028748443, 0.13812969189853194, -0.20367366949869037, -0.05336513803906494, 0.14149422393887967, -0.11274565523122759, 5.42456251075002, -0.24203119382682836, 0.19923900500919553, -0.21929908854766242, -0.02548667267967759, 0.040965140197428224, -10.108868758823299, -0.04040387641434644, 0.2988176083032231, 0.23581544565715526, -0.26565382299666723, 0.21356729609351444, 0.02037013934892774, 0.09008745163382564]<11> *source"FEA""<22>!1*solve_time156.87209296226501<EFBFBD>#<23>!<21>1*objectives{"rel_filtered_rms_40_vs_20": 6.0980229330058116, "rel_filtered_rms_60_vs_20": 14.383230997267628, "mfg_90_optician_workload": 30.467606062490262}<7D>"<22>5<>)zernike_rel_coeffs_4[-93.80143412836854, 3.885486600886317, -0.0996379710082559, 19.369319927673214, -0.6389074738294249, -6.081852345515233, -14.110779449275586, 0.463683340932562, 10.391175879967895, 0.2564737917908163, 8.238233223821066, -0.152131952148717, -10.651112174210137, -0.08639158739164979, -21.9515384361115, -5.049118907852396, -0.10360056396772654, -17.200028428783206, -0.18920116129320289, 2.9089677452257527, 0.07683620089167058, 4.893476634019711, 0.12871171207934984, 7.908784346216226, 0.014780608300862747, 0.40682376212360855, -0.10869901605547266, 0.4548404015972225, 0.3567073947292117, 0.08649214173818781, 4.775558699677949, 0.039159866941996996, -8.608006122568108, -0.06978514337916043, 2.742732675104006, -0.011846331459581928, 3.3098973275211794, -0.17994444377559904, -4.263596596513391, -0.044597886093849, 0.48544768429855517, 0.011335205524378562, -7.330149358909655, 0.04688249612043944, -0.45346950453514395, -0.4209051760153697, -0.10894976970251682, -1.6977920614672322, 0.036041721425425985, 4.803758541750697] <03> <0B><07><03>
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!solve_time !objectives -zernike_coeffs_4 -zernike_coeffs_3 -zernike_coeffs_2 -zernike_coeffs_1  source !solve_time ! objectives
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