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PAGP: A physics-assisted Gaussian process framework with active learning
  for forward and inverse problems of partial differential equations

PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations

6 April 2022
Jiahao Zhang
Shiqi Zhang
Guang Lin
ArXivPDFHTML

Papers citing "PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations"

3 / 3 papers shown
Title
Random Grid Neural Processes for Parametric Partial Differential
  Equations
Random Grid Neural Processes for Parametric Partial Differential Equations
Arnaud Vadeboncoeur
Ieva Kazlauskaite
Y. Papandreou
F. Cirak
Mark Girolami
Ömer Deniz Akyildiz
AI4CE
35
11
0
26 Jan 2023
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain
  Decomposition
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition
E. Kharazmi
Zhongqiang Zhang
George Karniadakis
135
510
0
11 Mar 2020
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian
  Process: A New Insight into Machine Learning Applications
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications
Yun Yuan
X. Yang
Zhao Zhang
Shandian Zhe
AI4CE
39
96
0
06 Feb 2020
1