[4F1-OS-30a] OS-30

[4F1-OS-30a-04] Shape Generative AI Considering Heat Diffusion Using Physics-Informed Neural Networks (PINN) and Conditional Variational Autoencoders (CVAE)

〇Tomofumi Shimokawa¹, Koji Matsumoto¹, Mitsunori Kamimura¹, Koji Iwayama^2,3, Takayuki Onojima³, Takashi Imai^2,3 (1. Toyota Motor Corporation, 2. Faculty of Data Science, Shiga University, 3. Data Science and AI Innovation Research Promotion Center, Shiga University)

Keywords:Physics-Informed Neural Networks, Design Assistance, Generative Design AI, Machine Learning, Deep Learning, Conditional Variational Autoencoder

Conventional shape generation techniques utilize Variational Autoencoders (VAE) to generate shapes based on statistical probabilities, which diverges from approaches where designers generate shapes based on physical quantities. Therefore, this study demonstrates that by combining Physics-Informed Neural Networks (PINN) with Conditional Variational Autoencoders (CVAE), it is possible to generate shapes based on the effects of thermal diffusion. This method is expected to promote shape generation that takes physical characteristics into account.