DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling

10 February 2025

Papers citing "DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling"

34 / 34 papers shown

Title
Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Lothar Heimbach Sebastian Kaltenbach Petr Karnakov Francis J. Alexander Petros Koumoutsakos AI4CE 93 0 0 16 May 2025
Physics-Informed Geometry-Aware Neural Operator Weiheng Zhong Hadi Meidani AI4CE 63 4 0 02 Aug 2024
Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography Vincent C. Scholz Yaohua Zang P. Koutsourelakis 66 3 0 30 Jul 2024
Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media Matthaios Chatzopoulos P. Koutsourelakis AI4CE 55 3 0 29 May 2024
ParticleWNN: a Novel Neural Networks Framework for Solving Partial Differential Equations Yaohua Zang Gang Bao 75 4 0 21 May 2023
Physics informed WNO N. N. Tapas Tripura S. Chakraborty 50 28 0 12 Feb 2023
Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning Hrishikesh Viswanath Md Ashiqur Rahman Abhijeet Vyas Andrey Shor Beatriz Medeiros Stephanie Hernandez S. Prameela Aniket Bera PINN AI4CE 64 5 0 30 Jan 2023
Random Grid Neural Processes for Parametric Partial Differential Equations Arnaud Vadeboncoeur Ieva Kazlauskaite Y. Papandreou F. Cirak Mark Girolami Ömer Deniz Akyildiz AI4CE 53 11 0 26 Jan 2023
Physics-informed Information Field Theory for Modeling Physical Systems with Uncertainty Quantification A. Alberts Ilias Bilionis 62 12 0 18 Jan 2023
Amortized Variational Inference: A Systematic Review Ankush Ganguly Sanjana Jain Ukrit Watchareeruetai 56 15 0 22 Sep 2022
Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems Sebastian Kaltenbach P. Perdikaris P. Koutsourelakis 50 24 0 06 Sep 2022
Fully probabilistic deep models for forward and inverse problems in parametric PDEs Arnaud Vadeboncoeur Ömer Deniz Akyildiz Ieva Kazlauskaite Mark Girolami F. Cirak AI4CE 42 18 0 09 Aug 2022
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries Zong-Yi Li Daniel Zhengyu Huang Burigede Liu Anima Anandkumar AI4CE 152 264 0 11 Jul 2022
Physics-Informed Deep Neural Operator Networks S. Goswami Aniruddha Bora Yue Yu George Karniadakis PINN AI4CE 60 102 0 08 Jul 2022
Learning Deep Implicit Fourier Neural Operators (IFNOs) with Applications to Heterogeneous Material Modeling Huaiqian You Quinn Zhang Colton J. Ross Chung-Hao Lee Yue Yu AI4CE 62 99 0 15 Mar 2022
Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers John Guibas Morteza Mardani Zong-Yi Li Andrew Tao Anima Anandkumar Bryan Catanzaro 53 234 0 24 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 89 399 0 06 Nov 2021
Multiwavelet-based Operator Learning for Differential Equations Gaurav Gupta Xiongye Xiao P. Bogdan 152 206 0 28 Sep 2021
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets Sizhuang He Hanwen Wang P. Perdikaris AI4CE 73 685 0 19 Mar 2021
Bayesian neural networks for weak solution of PDEs with uncertainty quantification Xiaoxuan Zhang K. Garikipati AI4CE 58 12 0 13 Jan 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 433 2,355 0 18 Oct 2020
A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables Maximilian Rixner P. Koutsourelakis AI4CE 54 21 0 02 Jun 2020
Neural Operator: Graph Kernel Network for Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar 133 718 0 07 Mar 2020
Physics-informed deep learning for incompressible laminar flows Chengping Rao Hao Sun Yang Liu PINN AI4CE 88 223 0 24 Feb 2020
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 254 10,591 0 17 Feb 2020
Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems Sebastian Kaltenbach P. Koutsourelakis AI4CE 111 35 0 30 Dec 2019
Variational Physics-Informed Neural Networks For Solving Partial Differential Equations E. Kharazmi Z. Zhang George Karniadakis 52 241 0 27 Nov 2019
On Robustness of Neural Ordinary Differential Equations Hanshu Yan Jiawei Du Vincent Y. F. Tan Jiashi Feng OOD 62 140 0 12 Oct 2019
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 177 2,082 0 08 Oct 2019
DeepMoD: Deep learning for Model Discovery in noisy data G. Both Subham Choudhury P. Sens R. Kusters 36 115 0 20 Apr 2019
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data Yinhao Zhu N. Zabaras P. Koutsourelakis P. Perdikaris PINN AI4CE 70 860 0 18 Jan 2019
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems E. Weinan Ting Yu 110 1,373 0 30 Sep 2017
DGM: A deep learning algorithm for solving partial differential equations Justin A. Sirignano K. Spiliopoulos AI4CE 72 2,048 0 24 Aug 2017
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation C. Qi Hao Su Kaichun Mo Leonidas Guibas 3DH 3DPC 3DV PINN 413 14,191 0 02 Dec 2016