Towards the Development of Entropy-Based Anomaly Detection in an Astrophysics Simulation
arXiv:2009.02430
Abstract
The use of AI and ML for scientific applications is currently a very exciting and dynamic field. Much of this excitement for HPC has focused on ML applications whose analysis and classification generate very large numbers of flops. Others seek to replace scientific simulations with data-driven surrogate models. But another important use case lies in the combination application of ML to improve simulation accuracy. To that end, we present an anomaly problem which arises from a core-collapse supernovae simulation. We discuss strategies and early successes in applying anomaly detection techniques from machine learning to this scientific simulation, as well as current challenges and future possibilities.
References in corpus (7)
- Scikit-learn: Machine Learning in Python
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Practical Bayesian Optimization of Machine Learning Algorithms
- Inside the Supernova: A Powerful Convective Engine
- Two-dimensional hydrodynamic core-collapse supernova simulations with spectral neutrino transport. I. Numerical method and results for a 15 M_sun star
- 3-Dimensional Core-Collapse
- Exascale Deep Learning for Scientific Inverse Problems