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 This paper is available on arxiv under CC BY 4.0 DEED license. 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. Authors: 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 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 Physics-Informed Deep Learning Framework Physics-Informed Deep Learning Framework Learning Problems for Earthquakes on Rate-and-State Faults Learning Problems for Earthquakes on Rate-and-State Faults 2D Verification, Validation and Applications 2D Verification, Validation and Applications Summary and Future Work and References 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 This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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Integrating Physics-Informed Neural Networks for Earthquake Modeling: Summary & References

Integrating Physics-Informed Neural Networks for Earthquake Modeling: Quakes on Rate & State Faults

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Integrating Physics-Informed Neural Networks for Earthquake Modeling: 2D Verification, Applications

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