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Tackling the Curse of Dimensionality in Fractional and Tempered
  Fractional PDEs with Physics-Informed Neural Networks

Tackling the Curse of Dimensionality in Fractional and Tempered Fractional PDEs with Physics-Informed Neural Networks

17 June 2024
Zheyuan Hu
Kenji Kawaguchi
Zhongqiang Zhang
George Karniadakis
    AI4CE
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Papers citing "Tackling the Curse of Dimensionality in Fractional and Tempered Fractional PDEs with Physics-Informed Neural Networks"

4 / 4 papers shown
Title
Anant-Net: Breaking the Curse of Dimensionality with Scalable and Interpretable Neural Surrogate for High-Dimensional PDEs
Anant-Net: Breaking the Curse of Dimensionality with Scalable and Interpretable Neural Surrogate for High-Dimensional PDEs
Sidharth S. Menon
Ameya D. Jagtap
PINN
101
0
0
06 May 2025
Hutchinson Trace Estimation for High-Dimensional and High-Order
  Physics-Informed Neural Networks
Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks
Zheyuan Hu
Zekun Shi
George Karniadakis
Kenji Kawaguchi
AI4CE
PINN
44
22
0
22 Dec 2023
Bias-Variance Trade-off in Physics-Informed Neural Networks with
  Randomized Smoothing for High-Dimensional PDEs
Bias-Variance Trade-off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs
Zheyuan Hu
Zhouhao Yang
Yezhen Wang
George Karniadakis
Kenji Kawaguchi
44
9
0
26 Nov 2023
Physics-informed neural networks with hard constraints for inverse
  design
Physics-informed neural networks with hard constraints for inverse design
Lu Lu
R. Pestourie
Wenjie Yao
Zhicheng Wang
F. Verdugo
Steven G. Johnson
PINN
39
493
0
09 Feb 2021
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