Neural networks as smooth priors for inverse problems for PDEs

Type: Article

Publication Date: 2021-09-01

Citations: 10

DOI: https://doi.org/10.1016/j.jcmds.2021.100008

Locations

  • Journal of Computational Mathematics and Data Science - View
  • Uppsala University Publications (Uppsala University) - View - PDF

Similar Works

Action Title Year Authors
+ Neural network augmented inverse problems for PDEs 2017 Jens Berg
Kaj Nyström
+ Adaptive quadratures for nonlinear approximation of low-dimensional PDEs using smooth neural networks 2024 Alexandre Magueresse
Santiago Badia
+ Adaptive quadratures for nonlinear approximation of low-dimensional PDEs using smooth neural networks 2023 Alexandre Magueresse
Santiago Badia
+ PDF Chat Adaptive Quadratures for Nonlinear Approximation of Low-Dimensional Pdes Using Smooth Neural Networks 2023 Alexandre Magueresse
Santiago Badia
+ B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data 2020 Liu Yang
Xuhui Meng
George Em Karniadakis
+ Finite element interpolated neural networks for solving forward and inverse problems 2023 Santiago Badia
Wei Li
Alberto F. Martı́n
+ PDF Chat Regularity-Conforming Neural Networks (ReCoNNs) for solving Partial Differential Equations 2024 Jamie M. Taylor
David Pardo
Judit Muñoz‐Matute
+ PDF Chat Neural Networks in Numerical Analysis and Approximation Theory 2024 Gonzalo Romera
+ Deep neural networks for smooth approximation of physics with higher order and continuity B-spline base functions 2022 Kamil Doległo
Anna Paszyńska
Maciej Paszyński
Leszek Demkowicz
+ PDF Chat Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems 2022 Jeremy Yu
Lu Lu
Xuhui Meng
George Em Karniadakis
+ SPINN: Sparse, Physics-based, and Interpretable Neural Networks for PDEs. 2021 Amuthan Arunkumar Ramabathiran
Prabhu Ramachandran
+ Greedy Training Algorithms for Neural Networks and Applications to PDEs 2021 Jonathan W. Siegel
Qingguo Hong
Xianlin Jin
Wenrui Hao
Jinchao Xu
+ Strong Solutions for PDE-Based Tomography by Unsupervised Learning 2021 Leah Bar
Nir Sochen
+ Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next 2022 Salvatore Cuomo
Vincenzo Schiano Di Cola
Fabio Giampaolo
Gianluigi Rozza
Maziar Raissi
Francesco Piccialli
+ Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions 2020 Philipp Grohs
Lukas Herrmann
+ Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions 2020 Philipp Grohs
Lukas Herrmann
+ Robust Physics Informed Neural Networks 2024 Marcin Łoś
Maciej Paszyński
+ Numerical approximation of inverse problems for PDEs via neural network augmentation 2020 Dillon Victor Paul Montag
+ Neural‐network‐based regularization methods for inverse problems in imaging 2024 Andreas Habring
Martin Höller
+ Bayesian neural networks for weak solution of PDEs with uncertainty quantification 2021 Xiaoxuan Zhang
Krishna Garikipati