Abstract

The advent of attention neural networks architecture and models, or more generally generative adversarial networks (GANs), have opened new avenues for training neural networks from low volume of data in a truly efficient way to gain physics based predictive capabilities. One of the key practical obstacle for leveraging engineering simulation results to train neural networks is the requirement for running a significant number of FEA or CFD solver simulations to reach a useful AI-based level of predicting capability level. The ability of training neural networks does exist, but the volume of FEA or CFD results needed limits us to very few practical engineering problems or to problems for which a system-level physics model is accurate enough. The GAN framework introduced by Ian J. Goodfellow in 2014 for estimating generative models via an adversarial process can be leveraged to significantly reduce the number of FEA or CFD solver simulations while resulting in very accurate results. In this paper, we show some practical applications where the adversarial framework is used to create reduced order models and virtual sensors from complex engineering simulations in a practical and manageable timeframe and with more reasonable compute resources. Moreover, the resulting AI agents (deep neural networks) can serve as an engineering basis to detect anomalies in real world applications by contrasting engineering simulation model results and real-time telemetry. The unique combination of GAN-based reduced order models or virtual sensors learning from engineering solvers can also recognize the onset of specific failure modes and allow time for operations to change course and avoid failures. We will outline how the generative model captures the engineering simulation data distribution, and how the discriminative model estimates the probability that a new sample came from the training data, providing a great foundation for leveraging failure mode simulations in real world applications.