Adversarial uncertainty quantification in physics-informed neural networks
Yibo Yang,Paris Perdikaris +1 more
TL;DR: A deep learning framework for quantifying and propagating uncertainty in systems governed by non-linear differential equations using physics-informed neural networks uses latent variable models to construct probabilistic representations for the system states, and puts forth an adversarial inference procedure for training them on data.
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About: This article is published in Journal of Computational Physics. The article was published on 01 Oct 2019. and is currently open access. The article focuses on the topics: Propagation of uncertainty & Uncertainty quantification.
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Citations
Physics-informed machine learning
George Em Karniadakis,Ioannis G. Kevrekidis,Lu Lu,Paris Perdikaris,Sifan Wang,Liu Yang +5 more
- 01 Jun 2021
TL;DR: Some of the prevailing trends in embedding physics into machine learning are reviewed, some of the current capabilities and limitations are presented and diverse applications of physics-informed learning both for forward and inverse problems, including discovering hidden physics and tackling high-dimensional problems are discussed.
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A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
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TL;DR: This study reviews recent advances in UQ methods used in deep learning and investigates the application of these methods in reinforcement learning (RL), and outlines a few important applications of UZ methods.
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Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
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TL;DR: A comprehensive review of the literature on physics-informed neural networks can be found in this article , where the primary goal of the study was to characterize these networks and their related advantages and disadvantages, as well as incorporate publications on a broader range of collocation-based physics informed neural networks.
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
TL;DR: This paper provides a methodology that incorporates the governing equations of the physical model in the loss/likelihood functions of the model predictive density and the reference conditional density as a minimization problem of the reverse Kullback-Leibler (KL) divergence.
Physics-Informed Neural Networks for Heat Transfer Problems
TL;DR: In this paper, physics-informed neural networks (PINNs) have been applied to various prototype heat transfer problems, targeting in particular realistic conditions not readily tackled with traditional computational methods.
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