Data Driven Governing Equations Approximation Using Deep Neural Networks

13 November 2018

Papers citing "Data Driven Governing Equations Approximation Using Deep Neural Networks"

50 / 52 papers shown

Title
Physics-informed Multiple-Input Operators for efficient dynamic response prediction of structures Bilal Ahmed Yuqing Qiu Diab W. Abueidda Waleed El-Sekelly Tarek Abdoun M. Mobasher AI4CE 39 0 0 11 May 2025
Identifying Unknown Stochastic Dynamics via Finite expression methods Senwei Liang Chunmei Wang Xingjian Xu 28 0 0 09 Apr 2025
Optimized Dynamic Mode Decomposition for Reconstruction and Forecasting of Atmospheric Chemistry Data Meghana Velegar Christoph Keller J. Nathan Kutz 33 1 0 13 Apr 2024
Hierarchical deep learning-based adaptive time-stepping scheme for multiscale simulations Asif Hamid Danish Rafiq S. A. Nahvi M. A. Bazaz AI4CE 42 1 0 10 Nov 2023
Flow Map Learning for Unknown Dynamical Systems: Overview, Implementation, and Benchmarks V. Churchill D. Xiu AI4CE 28 10 0 20 Jul 2023
On the Identifiablility of Nonlocal Interaction Kernels in First-Order Systems of Interacting Particles on Riemannian Manifolds Sui Tang Malik Tuerkoen Hanming Zhou 35 4 0 21 May 2023
On the effectiveness of neural priors in modeling dynamical systems Sameera Ramasinghe Hemanth Saratchandran Violetta Shevchenko Simon Lucey 29 2 0 10 Mar 2023
Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks Shuai Han Lukas Stelz Horst Stoecker Lei Wang Kai Zhou AI4CE PINN 34 9 0 17 Feb 2023
On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators Christian Moya Guang Lin Tianqiao Zhao Meng Yue 32 8 0 29 Jan 2023
SPADE4: Sparsity and Delay Embedding based Forecasting of Epidemics Esha Saha L. Ho Giang Tran 36 5 0 11 Nov 2022
Data-driven synchronization-avoiding algorithms in the explicit distributed structural analysis of soft tissue G. Tong Daniele E. Schiavazzi 32 3 0 05 Jul 2022
Learning nonparametric ordinary differential equations from noisy data Kamel Lahouel Michael L. Wells Victor Rielly Ethan Lew David M Lovitz Bruno Jedynak 26 5 0 30 Jun 2022
VPNets: Volume-preserving neural networks for learning source-free dynamics Aiqing Zhu Beibei Zhu Jiawei Zhang Yifa Tang Jian-Dong Liu 34 3 0 29 Apr 2022
PARC: Physics-Aware Recurrent Convolutional Neural Networks to Assimilate Meso-scale Reactive Mechanics of Energetic Materials Phong C. H. Nguyen Y. Nguyen Joseph B. Choi P. Seshadri H. Udaykumar Stephen Seung-Yeob Baek AI4CE 21 16 0 04 Apr 2022
Online Weak-form Sparse Identification of Partial Differential Equations Daniel Messenger E. Dall’Anese David M. Bortz 41 13 0 08 Mar 2022
Robust Modeling of Unknown Dynamical Systems via Ensemble Averaged Learning V. Churchill Steve Manns Zhen Chen D. Xiu AI4CE 29 9 0 07 Mar 2022
DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories Christian Moya Shiqi Zhang Meng Yue Guang Lin 22 42 0 15 Feb 2022
Physics-informed neural networks for solving parametric magnetostatic problems Andrés Beltrán-Pulido Ilias Bilionis D. Aliprantis 27 34 0 08 Feb 2022
Modeling unknown dynamical systems with hidden parameters Xiaohan Fu Weize Mao L. Chang D. Xiu 21 5 0 03 Feb 2022
Constructing Neural Network-Based Models for Simulating Dynamical Systems Christian Møldrup Legaard Thomas Schranz G. Schweiger Ján Drgovna Basak Falay C. Gomes Alexandros Iosifidis M. Abkar P. Larsen PINN AI4CE 33 93 0 02 Nov 2021
DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method Zhongjian Wang Jack Xin Zhiwen Zhang 39 15 0 02 Nov 2021
DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks Christian Moya Guang Lin AI4CE PINN 59 37 0 09 Sep 2021
Physics-integrated hybrid framework for model form error identification in nonlinear dynamical systems Shailesh Garg S. Chakraborty B. Hazra 46 20 0 01 Sep 2021
Adaptive Group Lasso Neural Network Models for Functions of Few Variables and Time-Dependent Data L. Ho Nicholas Richardson Giang Tran 18 3 0 24 Aug 2021
Model-free inference of unseen attractors: Reconstructing phase space features from a single noisy trajectory using reservoir computing André Röhm D. Gauthier Ingo Fischer 58 38 0 06 Aug 2021
Deep Neural Network Modeling of Unknown Partial Differential Equations in Nodal Space Zhen Chen V. Churchill Kailiang Wu D. Xiu AI4CE 14 47 0 07 Jun 2021
Learning particle swarming models from data with Gaussian processes Jinchao Feng Charles Kulick Yunxiang Ren Sui Tang 26 5 0 04 Jun 2021
Efficient training of physics-informed neural networks via importance sampling M. A. Nabian R. J. Gladstone Hadi Meidani DiffM PINN 71 222 0 26 Apr 2021
Model Order Reduction based on Runge-Kutta Neural Network Qinyu Zhuang Juan M Lorenzi H. Bungartz D. Hartmann 11 14 0 25 Mar 2021
The Discovery of Dynamics via Linear Multistep Methods and Deep Learning: Error Estimation Q. Du Yiqi Gu Haizhao Yang Chao Zhou 26 20 0 21 Mar 2021
Multi-objective discovery of PDE systems using evolutionary approach M. Maslyaev A. Hvatov 23 5 0 11 Mar 2021
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data Mohamed Aziz Bhouri P. Perdikaris 28 20 0 04 Mar 2021
A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate Hongwei Guo X. Zhuang Timon Rabczuk AI4CE 19 433 0 04 Feb 2021
Deep learning prediction of patient response time course from early data via neural-pharmacokinetic/pharmacodynamic modeling James Lu B. Bender Jin Y. Jin Y. Guan 31 46 0 22 Oct 2020
Discovery of Governing Equations with Recursive Deep Neural Networks Jia Zhao Jarrod Mau PINN 30 6 0 24 Sep 2020
Bayesian Sparse learning with preconditioned stochastic gradient MCMC and its applications Yating Wang Wei Deng Guang Lin 16 13 0 29 Jun 2020
Deep learning of free boundary and Stefan problems Sizhuang He P. Perdikaris 27 80 0 04 Jun 2020
Deep-learning of Parametric Partial Differential Equations from Sparse and Noisy Data Hao Xu Dongxiao Zhang Junsheng Zeng 25 57 0 16 May 2020
RotEqNet: Rotation-Equivariant Network for Fluid Systems with Symmetric High-Order Tensors Liyao (Mars) Gao Yifan Du Hongshan Li Guang Lin 35 12 0 28 Apr 2020
Learning reduced systems via deep neural networks with memory Xiaohang Fu L. Chang D. Xiu 11 32 0 20 Mar 2020
Methods to Recover Unknown Processes in Partial Differential Equations Using Data Zhen Chen Kailiang Wu D. Xiu 22 3 0 05 Mar 2020
Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction Vincent Le Guen Nicolas Thome AI4CE PINN 89 288 0 03 Mar 2020
On generalized residue network for deep learning of unknown dynamical systems Zhen Chen D. Xiu AI4CE 19 46 0 23 Jan 2020
DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm Hao Xu Haibin Chang Dongxiao Zhang AI4CE 25 87 0 21 Jan 2020
Discovery of Dynamics Using Linear Multistep Methods Rachael Keller Q. Du 31 36 0 29 Dec 2019
Data-Driven Deep Learning of Partial Differential Equations in Modal Space Kailiang Wu D. Xiu 9 149 0 15 Oct 2019
Neural Dynamics on Complex Networks Chengxi Zang Fei Wang AI4CE 32 68 0 18 Aug 2019
EM-like Learning Chaotic Dynamics from Noisy and Partial Observations Duong Nguyen Said Ouala Lucas Drumetz Ronan Fablet 13 29 0 25 Mar 2019
PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network Zichao Long Yiping Lu Bin Dong AI4CE 31 542 0 30 Nov 2018
Using Deep Learning to Extend the Range of Air-Pollution Monitoring and Forecasting Philipp Haehnel Jakub Mareˇcek Julien Monteil Fearghal O'Donncha AI4CE 20 39 0 22 Oct 2018