Generative adversarial networks for real-time realistic physics simulations

Mark Harlow; Matthew Martinez; Xiaoyan Han; Stoian Borissov; Jason Hill

doi:10.1117/12.2663951

12 June 2023 Generative adversarial networks for real-time realistic physics simulations

Mark Harlow, Matthew Martinez, Xiaoyan Han, Stoian Borissov, Jason Hill

Proceedings Volume 12536, Thermosense: Thermal Infrared Applications XLV; 125360K (2023) https://doi.org/10.1117/12.2663951
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Lynntech is seeking to develop real-time realistic nondestructive evaluation (NDE) and structural health monitoring (SHM) physics-based simulations, and automated data reduction/analysis methods, for large datasets. Recently, computational efficient Neural Network based simulations have demonstrated the possibility to synthesize data with an orders-of-magnitude increase in speed compared to standard computational techniques [1,2]. In this contribution, we report our initial experimental results for our Generative Adversarial Network for Realistic Physics Simulations, or GAN4RPS.

Conference Presentation

Citation Download Citation

Mark Harlow, Matthew Martinez, Xiaoyan Han, Stoian Borissov, and Jason Hill "Generative adversarial networks for real-time realistic physics simulations", Proc. SPIE 12536, Thermosense: Thermal Infrared Applications XLV, 125360K (12 June 2023); https://doi.org/10.1117/12.2663951

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