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Summary & Details

Dynamic Surrogate Modeling romAI and Fast Physics Prediction physicsAI: Applications in CFD

動的サロゲートモデルromAIと高速物理予測physicsAIのCFDへの適用

Delivery: Available on this site

Format: PDF

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

Publication code: 20244758

Paper/Info type: Forum Text (Online)

No.24-W4

Pages: 1-36(Total 36 p)

Date of publication: Jan 2025

Publisher: JSAE

Language: Japanese

Event: JSAE Forum 2024 (Winter)

Detailed Information

Category(J)	PPT資料 Translation
Category(E)	PPT slides
Author(J)	1) 池田公輔
Author(E)	1) Kosuke IKEDA
Affiliation(J)	1) アルテアエンジニアリング株式会社
Affiliation(E)	1) Altair Engineering, Ltd.
Abstract(J)	微分方程式とDeep Learningを組み合わせた動的サロゲートモデル化手法であるromAIと、Geometric Deep Learningを用いて形状変更に対する高速性能予測を実現するphysicsAIについて紹介します。また、これらの手法をCFDに適用した事例についても解説します。 Translation
Abstract(E)	This presentation introduces romAI, a dynamic surrogate modeling method that combines differential equations with deep learning, and physicsAI, which employs geometric deep learning for high-speed performance prediction under shape variations. Application examples in CFD will also be discussed.

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