BiLO: Bilevel Local Operator Learning for PDE inverse problems

27 April 2024

Papers citing "BiLO: Bilevel Local Operator Learning for PDE inverse problems"

36 / 36 papers shown

Title
PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks Sizhuang He Bowen Li Yuhan Chen P. Perdikaris AI4CE PINN 97 32 0 01 Feb 2024
Personalized Predictions of Glioblastoma Infiltration: Mathematical Models, Physics-Informed Neural Networks and Multimodal Scans Ray Zirui Zhang Ivan Ezhov Michal Balcerak Andy Zhu Benedikt Wiestler Bjoern Menze John S. Lowengrub AI4CE 74 6 0 28 Nov 2023
Correcting model misspecification in physics-informed neural networks (PINNs) Zongren Zou Xuhui Meng George Karniadakis PINN 57 42 0 16 Oct 2023
An Expert's Guide to Training Physics-informed Neural Networks Sizhuang He Shyam Sankaran Hanwen Wang P. Perdikaris PINN 53 106 0 16 Aug 2023
Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression Zhen Zhang Zongren Zou E. Kuhl George Karniadakis 48 43 0 16 Jul 2023
PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems S. Mao R. Dong Lu Lu K. M. Yi Sizhuang He P. Perdikaris 43 16 0 18 May 2023
In-Context Operator Learning with Data Prompts for Differential Equation Problems Liu Yang Siting Liu Tingwei Meng Stanley J. Osher 84 63 0 17 Apr 2023
Machine Learning for Partial Differential Equations Steven L. Brunton J. Nathan Kutz AI4CE 62 20 0 30 Mar 2023
Physics-informed neural networks for solving forward and inverse problems in complex beam systems Taniya Kapoor Hongrui Wang A. Núñez R. Dollevoet AI4CE PINN 42 46 0 02 Mar 2023
Neural Inverse Operators for Solving PDE Inverse Problems Roberto Molinaro Yunan Yang Bjorn Engquist Siddhartha Mishra AI4CE 42 38 0 26 Jan 2023
Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems Derick Nganyu Tanyu Jianfeng Ning Tom Freudenberg Nick Heilenkötter A. Rademacher U. Iben Peter Maass AI4CE 51 34 0 06 Dec 2022
Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems Sebastian Kaltenbach P. Perdikaris P. Koutsourelakis 52 24 0 06 Sep 2022
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks Chen-Chun Wu Min Zhu Qinyan Tan Yadhu Kartha Lu Lu 65 372 0 21 Jul 2022
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning Thomas O'Leary-Roseberry Peng Chen Umberto Villa Omar Ghattas AI4CE 48 40 0 21 Jun 2022
Critical Investigation of Failure Modes in Physics-informed Neural Networks S. Basir Inanc Senocak PINN AI4CE 55 20 0 20 Jun 2022
Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport Lu Lu R. Pestourie Steven G. Johnson Giuseppe Romano AI4CE 47 107 0 14 Apr 2022
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators Jaideep Pathak Shashank Subramanian P. Harrington S. Raja Ashesh Chattopadhyay ... Zong-Yi Li Kamyar Azizzadenesheli Pedram Hassanzadeh K. Kashinath Anima Anandkumar AI4Cl 208 682 0 22 Feb 2022
Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling Ivan Ezhov Kevin Scibilia Katharina Franitza Felix Steinbauer Suprosanna Shit ... Diana Waldmannstetter Martin J. Menten M. Metz Benedikt Wiestler Bjoern Menze 69 27 0 07 Nov 2021
Physics-Informed Neural Operator for Learning Partial Differential Equations Zong-Yi Li Hongkai Zheng Nikola B. Kovachki David Jin Haoxuan Chen Burigede Liu Kamyar Azizzadenesheli Anima Anandkumar AI4CE 92 406 0 06 Nov 2021
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems Jeremy Yu Lu Lu Xuhui Meng George Karniadakis PINN AI4CE 61 462 0 01 Nov 2021
Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-fidelity Data Fusion S. Basir Inanc Senocak PINN AI4CE 71 70 0 30 Sep 2021
Characterizing possible failure modes in physics-informed neural networks Aditi S. Krishnapriyan A. Gholami Shandian Zhe Robert M. Kirby Michael W. Mahoney PINN AI4CE 102 633 0 02 Sep 2021
Neural Operator: Learning Maps Between Function Spaces Nikola B. Kovachki Zong-Yi Li Burigede Liu Kamyar Azizzadenesheli K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 99 446 0 19 Aug 2021
Seismic wave propagation and inversion with Neural Operators Yan Yang Angela F. Gao J. Castellanos Zachary E. Ross Kamyar Azizzadenesheli R. Clayton 39 72 0 11 Aug 2021
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations Benjamin Moseley Andrew Markham T. Nissen‐Meyer PINN 71 221 0 16 Jul 2021
Inverse-Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networks Suryanarayana Maddu D. Sturm Christian L. Müller I. Sbalzarini AI4CE 61 83 0 02 Jul 2021
Efficient training of physics-informed neural networks via importance sampling M. A. Nabian R. J. Gladstone Hadi Meidani DiffM PINN 107 230 0 26 Apr 2021
Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks N. Sukumar Ankit Srivastava PINN AI4CE 111 252 0 17 Apr 2021
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets Sizhuang He Hanwen Wang P. Perdikaris AI4CE 77 695 0 19 Mar 2021
Physics-informed neural networks with hard constraints for inverse design Lu Lu R. Pestourie Wenjie Yao Zhicheng Wang F. Verdugo Steven G. Johnson PINN 89 515 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 470 2,384 0 18 Oct 2020
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism L. McClenny U. Braga-Neto PINN 72 458 0 07 Sep 2020
A Method for Representing Periodic Functions and Enforcing Exactly Periodic Boundary Conditions with Deep Neural Networks S. Dong Naxian Ni 76 136 0 15 Jul 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 231 779 0 13 Mar 2020
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 214 2,108 0 08 Oct 2019
DeepXDE: A deep learning library for solving differential equations Lu Lu Xuhui Meng Zhiping Mao George Karniadakis PINN AI4CE 95 1,521 0 10 Jul 2019