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Summary & Details

Toward Generalizable Graph Learning for 3D Engineering AI Explainable Workflows for CAE Mode Shape Classification and CFD Field Prediction

Delivery: Available on this site

Format: PDF

Price: Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880

Publication code: 20265249

Paper/Info type: Proceedings (Spring)

No.58-26

Pages: 1-8(Total 8 p)

Date of publication: May 2026

Publisher: JSAE

Language: English

Event: 2026 JSAE Annual Congress (Spring)

Detailed Information

Author(J)	1) Tong Duy Son, 2) Kohta Sugiura, 3) Marc Brughmans, 4) Andrey Hense, 5) Zhihao Liu, 6) Amirthalakshmi Veeraraghavan, 7) Ajinkya Bhave, 8) Jay Masters, 9) Paolo di Carlo, 10) Theo Geluk
Author(E)	1) Tong Duy Son, 2) Kohta Sugiura, 3) Marc Brughmans, 4) Andrey Hense, 5) Zhihao Liu, 6) Amirthalakshmi Veeraraghavan, 7) Ajinkya Bhave, 8) Jay Masters, 9) Paolo di Carlo, 10) Theo Geluk
Affiliation(J)	1) Siemens Digital Industries Software, 2) Siemens Digital Industries Software, 3) Siemens Digital Industries Software, 4) Siemens Digital Industries Software, 5) Siemens Digital Industries Software, 6) Siemens Digital Industries Software, 7) Siemens Digital Industries Software, 8) Siemens Digital Industries Software, 9) Siemens Digital Industries Software, 10) Siemens Digital Industries Software
Affiliation(E)	1) Siemens Digital Industries Software, 2) Siemens Digital Industries Software, 3) Siemens Digital Industries Software, 4) Siemens Digital Industries Software, 5) Siemens Digital Industries Software, 6) Siemens Digital Industries Software, 7) Siemens Digital Industries Software, 8) Siemens Digital Industries Software, 9) Siemens Digital Industries Software, 10) Siemens Digital Industries Software
Abstract(E)	PhysicsAI delivers fast physics predictions enabling engineering teams to generate design variations rapidly. PhysicsAI learns physics behavior using Graph Neural Networks (GNNs) trained on mesh geometries and CAD models data. Engineers can explore various design variations, optimize parameters, and accelerate innovation. We present two applications: (1) External aerodynamic drag prediction using CFD simulation data, achieving high accuracy while reducing computation time from hours to minutes; (2) Vibration mode shape recognition and classification for NVH optimization, demonstrating expert-level accuracy on complex automotive structures. Validation from comprehensive automotive datasets will be presented.

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