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Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations

Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations

Wenqian Chen
P. Stinis
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Papers citing "Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations"

4 / 4 papers shown
Title
Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling
Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling
Adrienne M. Propp
Daniel M. Tartakovsky
AI4CE
26
2
0
16 Oct 2024
Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-fidelity Data Fusion
Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-fidelity Data Fusion
S. Basir
Inanc Senocak
PINN
AI4CE
34
68
0
30 Sep 2021
Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities
Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities
Mengwu Guo
Andrea Manzoni
Maurice Amendt
Paolo Conti
J. Hesthaven
68
95
0
26 Feb 2021
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
Sifan Wang
Hanwen Wang
P. Perdikaris
131
438
0
18 Dec 2020