Aerodynamic Surrogate & Deep Optimization
ai_design.title
ai_design.subtitle
Physics-Informed Surrogate FNO Model
INPUT: Shape Camber & Thickness (33%)|AI SOLVER LATENCY: 1.8 ms
Stage 01 // Geometric Space
ai_design.pipeline.input.title
Thickness Chord:5.83%
Mesh Dimensions:6,144 control cells
Stage 02 // Neural FNO
ai_design.pipeline.model.title
Model Architecture:Physics-Informed FNO
Loss Target:L_nse + L_pde
Stage 03 // Real-Time Flow
ai_design.pipeline.output.title
Drag Coeff (Cd):0.0438
Lift Coeff (Cl):0.3253
ai_design.pipeline.speed_note
Fully continuous geometric inference
Engineering Excellence
ai_design.key_features_title
ai_design.cards.speedup.title
ai_design.cards.speedup.description
ai_design.cards.automation.title
ai_design.cards.automation.description
ai_design.cards.customization.title
ai_design.cards.customization.description
Solving Bottlenecks
ai_design.comparison_title
Method 01 // Classic
ai_design.comparison.traditional.title
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Method 02 // Neural
ai_design.comparison.ai.title
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