A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations
[article]
2021
arXiv
pre-print
Preserved Fulltext
Physics-informed neural networks (PINNs) are an increasingly powerful way to solve partial differential equations, generate digital twins, and create neural surrogates of physical models. ...
We end by focusing on a complex multiphysics example, the Doyle-Fuller-Newman (DFN) Model, and showcase how this PDE can be formulated and solved with NeuralPDE. ...
Physics-Informed Neural Networks (PINNs)
The PINN Training Problem The basis of our physical models will be partial differential equations (PDEs). ...
arXiv:2107.09443v1
fatcat:22zfxicti5ed3msl6lcbzmynga
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2022 pre-print arXiv:2201.05624v2 fatcat:3fz7t34y7revjohgaq7o47bdhiOther Versions
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself. ...
also attempts to incorporate publications on a larger variety of issues, including physics-constrained neural networks (PCNN), where the initial or boundary conditions are directly embedded in the NN ...
A deep neural network can reduce approximation error by increasing network expressivity, but it can also produce a large generalization error. ...
arXiv:2201.05624v3
fatcat:elmdoax7ongblim3cbvkj2pdxi
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
[article]
2022
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Physic-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself. ...
also attempts to incorporate publications on a larger variety of issues, including physics-constrained neural networks (PCNN), where the initial or boundary conditions are directly embedded in the NN ...
A deep neural network can reduce approximation error by increasing network expressivity, but it can also produce a large generalization error. ...
doi:10.48550/arxiv.2201.05624
fatcat:psynxr3k75bwpm437zl7ua5ndy