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Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges
The computerized simulations of physical and socio-economic systems have proliferated in
the past decade, at the same time, the capability to develop high-fidelity system predictive …
the past decade, at the same time, the capability to develop high-fidelity system predictive …
Physics-informed machine learning
GE Karniadakis, IG Kevrekidis, L Lu… - Nature Reviews …, 2021 - nature.com
Despite great progress in simulating multiphysics problems using the numerical
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
Neural networks (NNs) are currently changing the computational paradigm on how to
combine data with mathematical laws in physics and engineering in a profound way …
combine data with mathematical laws in physics and engineering in a profound way …
Learning the solution operator of parametric partial differential equations with physics-informed DeepONets
S Wang, H Wang, P Perdikaris - Science advances, 2021 - science.org
Partial differential equations (PDEs) play a central role in the mathematical analysis and
modeling of complex dynamic processes across all corners of science and engineering …
modeling of complex dynamic processes across all corners of science and engineering …
Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems
T Tripura, S Chakraborty - Computer Methods in Applied Mechanics and …, 2023 - Elsevier
With massive advancements in sensor technologies and Internet-of-things (IoT), we now
have access to terabytes of historical data; however, there is a lack of clarity on how to best …
have access to terabytes of historical data; however, there is a lack of clarity on how to best …
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
S Wang, H Wang, P Perdikaris - Computer Methods in Applied Mechanics …, 2021 - Elsevier
Physics-informed neural networks (PINNs) are demonstrating remarkable promise in
integrating physical models with gappy and noisy observational data, but they still struggle …
integrating physical models with gappy and noisy observational data, but they still struggle …
A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations
A physics informed neural network (PINN) incorporates the physics of a system by satisfying
its boundary value problem through a neural network's loss function. The PINN approach …
its boundary value problem through a neural network's loss function. The PINN approach …
PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
Recently, the advent of deep learning has spurred interest in the development of physics-
informed neural networks (PINN) for efficiently solving partial differential equations (PDEs) …
informed neural networks (PINN) for efficiently solving partial differential equations (PDEs) …
[PDF][PDF] Integrating physics-based modeling with machine learning: A survey
J Willard, X Jia, S Xu, M Steinbach… - arxiv preprint arxiv …, 2020 - beiyulincs.github.io
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Integrating scientific knowledge with machine learning for engineering and environmental systems
J Willard, X Jia, S Xu, M Steinbach, V Kumar - ACM Computing Surveys, 2022 - dl.acm.org
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …