:::info
Authors:
(1) Cody Rucker, Department of Computer Science, University of Oregon and Corresponding author;
(2) Brittany A. Erickson, Department of Computer Science, University of Oregon and Department of Earth Sciences, University of Oregon.
:::
Table of Links
Abstract and 1. Context and Motivation
Physics-Informed Deep Learning Framework
Learning Problems for Earthquakes on Rate-and-State Faults
2D Verification, Validation and Applications
Summary and Future Work and References
4. 2D Verification, Validation and Applications
When computational methods for physical problems are used to address science questions, verification is an essential first step to ensure credible results [20, 12]. While validation with observational data is the focus of future work, we must first verify that our physics-informed deep learning framework is able to solve both forward and inverse problems to reasonable accuracy.
4.1. Verification with the Method of Manufactured Solutions
:::info
This paper is available on arxiv under CC BY 4.0 DEED license.
:::