Generative adversarial networks for real-time realistic physics simulations

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.