论文标题
在天体物理学模拟中开发基于熵的异常检测
Towards the Development of Entropy-Based Anomaly Detection in an Astrophysics Simulation
论文作者
论文摘要
目前，将AI和ML用于科学应用是一个非常令人兴奋和动态的领域。对HPC的这种兴奋大部分都集中在分析和分类产生大量失败的ML应用上。其他人则试图用数据驱动的替代模型代替科学模拟。但是另一个重要的用例在于ML的组合应用以提高模拟精度。为此，我们提出了一个异常问题，该问题是由核心折叠超新星模拟引起的。我们讨论了从机器学习到该科学模拟以及当前的挑战和未来可能性的策略和早期成功。
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.
