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[HTML][HTML] Methods for enabling real-time analysis in digital twins: A literature review
This paper presents a literature review on methods for enabling real-time analysis in digital
twins, which are virtual models of physical systems. The advantages of digital twins are …
twins, which are virtual models of physical systems. The advantages of digital twins are …
Characterizing possible failure modes in physics-informed neural networks
Recent work in scientific machine learning has developed so-called physics-informed neural
network (PINN) models. The typical approach is to incorporate physical domain knowledge …
network (PINN) models. The typical approach is to incorporate physical domain knowledge …
CAN-PINN: A fast physics-informed neural network based on coupled-automatic–numerical differentiation method
In this study, novel physics-informed neural network (PINN) methods for coupling
neighboring support points and their derivative terms which are obtained by automatic …
neighboring support points and their derivative terms which are obtained by automatic …
DeepXDE: A deep learning library for solving differential equations
Deep learning has achieved remarkable success in diverse applications; however, its use in
solving partial differential equations (PDEs) has emerged only recently. Here, we present an …
solving partial differential equations (PDEs) has emerged only recently. Here, we present an …
fPINNs: Fractional physics-informed neural networks
Physics-informed neural networks (PINNs), introduced in M. Raissi, P. Perdikaris, and G.
Karniadakis, J. Comput. Phys., 378 (2019), pp. 686--707, are effective in solving integer …
Karniadakis, J. Comput. Phys., 378 (2019), pp. 686--707, are effective in solving integer …
[PDF][PDF] Artificial neural network methods for the solution of second order boundary value problems
We present a method for solving partial differential equations using artificial neural networks
and an adaptive collocation strategy. In this procedure, a coarse grid of training points is …
and an adaptive collocation strategy. In this procedure, a coarse grid of training points is …
Prediction of porous media fluid flow using physics informed neural networks
Due to the explosion of the digital age of data, deep learning applications for different
physical sciences have gained momentum. In this paper, we implement a physics informed …
physical sciences have gained momentum. In this paper, we implement a physics informed …
A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate
physical simulations in which the intrinsic solution space falls into a subspace with a small …
physical simulations in which the intrinsic solution space falls into a subspace with a small …
Optimal control of PDEs using physics-informed neural networks
Physics-informed neural networks (PINNs) have recently become a popular method for
solving forward and inverse problems governed by partial differential equations (PDEs). By …
solving forward and inverse problems governed by partial differential equations (PDEs). By …
VC-PINN: Variable coefficient physics-informed neural network for forward and inverse problems of PDEs with variable coefficient
Z Miao, Y Chen - Physica D: Nonlinear Phenomena, 2023 - Elsevier
The paper proposes a deep learning method specifically dealing with the forward and
inverse problem of variable coefficient partial differential equations–variable coefficient …
inverse problem of variable coefficient partial differential equations–variable coefficient …