Fully probabilistic deep models for forward and inverse problems in
parametric PDEs

Fully probabilistic deep models for forward and inverse problems in parametric PDEs

9 August 2022

A. Vadeboncoeur

Ömer Deniz Akyildiz

Ieva Kazlauskaite

Papers citing "Fully probabilistic deep models for forward and inverse problems in parametric PDEs"

8 / 8 papers shown

Title
Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Abdolmehdi Behroozi Chaopeng Shen and Daniel Kifer AI4CE 17 0 0 13 May 2025
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
Dual Cone Gradient Descent for Training Physics-Informed Neural Networks Youngsik Hwang Dong-Young Lim AI4CE 28 2 0 27 Sep 2024
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling Alex Glyn-Davies A. Vadeboncoeur O. Deniz Akyildiz Ieva Kazlauskaite Mark Girolami PINN 58 0 0 10 Sep 2024
Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media Matthaios Chatzopoulos P. Koutsourelakis AI4CE 29 3 0 29 May 2024
Efficient Prior Calibration From Indirect Data O. Deniz Akyildiz M. Girolami Andrew M. Stuart A. Vadeboncoeur 36 1 0 28 May 2024
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
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 2,281 0 18 Oct 2020