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Weak neural variational inference for solving Bayesian inverse problems
  without forward models: applications in elastography

Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography

30 July 2024
Vincent C. Scholz
Yaohua Zang
P. Koutsourelakis
ArXivPDFHTML

Papers citing "Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography"

4 / 4 papers shown
Title
DGNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling
Yaohua Zang
P. Koutsourelakis
AI4CE
52
0
0
10 Feb 2025
Fourier Neural Operator for Parametric Partial Differential Equations
Fourier Neural Operator for Parametric Partial Differential Equations
Zong-Yi Li
Nikola B. Kovachki
Kamyar Azizzadenesheli
Burigede Liu
K. Bhattacharya
Andrew M. Stuart
Anima Anandkumar
AI4CE
203
2,254
0
18 Oct 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and
  Inverse PDE Problems with Noisy Data
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data
Liu Yang
Xuhui Meng
George Karniadakis
PINN
170
755
0
13 Mar 2020
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
117
503
0
11 Mar 2020
1