Machine learning for physics simulation anomaly detection

Adam Good; Howard Pritchard; Lissa Moore; Garrett Kenyon

doi:10.1117/12.2594732

1 August 2021 Machine learning for physics simulation anomaly detection

Adam Good, Howard Pritchard, Lissa Moore, Garrett Kenyon

Proceedings Volume 11843, Applications of Machine Learning 2021; 118430O (2021) https://doi.org/10.1117/12.2594732
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Abstract

Multi-physics hydrodynamic direct numerical simulations (DNS) are often computationally intensive, requiring significant computational resources to complete. For simulations requiring thousands of processors, the probability of anomalies occurring during a simulation is not insignificant. Since these simulations often run for a long time without human validation, such undetected anomalies can be costly. We will present results of our application of ML based techniques for early anomaly detection to hydrodynamics simulations. By treating the intermediate output of hydrodynamic simulations as images or videos, we could borrow ML techniques from computer vision for the task of anomaly detection. We generated a training dataset using CLAMR, a cell-based adaptive mesh refinement application which implements the shallow water equations. Modifications were done to the application to obtain a wider range of experiments for our dataset. By varying the mesh resolution, domain size, and the initial state of the simulation, we generated a range of experiments who’s states can be learned using computer vision techniques. Additionally, those same experiments could be run with anomalies injected during the simulation so our models could be trained to differentiate between nominal and anomalous simulation states. We also present ML models using PetaVision, a neuromorphic computing simulation toolkit, as well as other ML frameworks, and demonstrate that they can predict the state of a simulation at a succeeding time step based on the state of the DNS given results from a number of preceding time steps. We will further compare the relative performance of these approaches to early anomaly detection and potential next steps to applying these techniques to more complex, multi-physics DNS applications.

Conference Presentation

Citation Download Citation

Adam Good, Howard Pritchard, Lissa Moore, and Garrett Kenyon "Machine learning for physics simulation anomaly detection", Proc. SPIE 11843, Applications of Machine Learning 2021, 118430O (1 August 2021); https://doi.org/10.1117/12.2594732

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