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Physics-Informed Deep Learning for Wave Propagation and Full Waveform Inversions

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Overview

Year:
2021
Contributor:
Rasht-Behesht, Majid (Author)
Huber, Christian (Author)
Shukla, Khemraj (Author)
Karniadakis, George Em (Author)
Subject:
deep learning
Physics-Informed Neural Networks
DOI
https://doi.org/10.26300/x3wd-4k56

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Description

Abstract:
This Python code performs seismic inversions using Physics-Informed Neural Networks (PINNs), a meshless deep learning technique that solves the forward and inverse problems in parallel.

Content

Access Conditions

Use and Reproduction
Attribution 4.0 International (CC BY 4.0)
Rights
In Copyright

Citation

Rasht-Behesht, Majid, Huber, Christian, Shukla, Khemraj, et al., "Physics-Informed Deep Learning for Wave Propagation and Full Waveform Inversions" (2021). Brown University Open Data Collection. Brown Digital Repository. Brown University Library. https://doi.org/10.26300/x3wd-4k56

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Collection:

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    Brown University Open Data Collection

    This collection contains open and publicly-funded data sets created by Brown University faculty and student researchers. Increasingly, publishers, and funders are requiring that protocols, data sets, metadata, and code underlying published research be retained and preserved, their locations cited within …
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