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Physics-Incorporated Convolutional Recurrent Neural Networks for Source
  Identification and Forecasting of Dynamical Systems
v1v2v3 (latest)

Physics-Incorporated Convolutional Recurrent Neural Networks for Source Identification and Forecasting of Dynamical Systems

Neural Networks (NN), 2020
14 April 2020
Priyabrata Saha
Saurabh Dash
Saibal Mukhopadhyay
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Physics-Incorporated Convolutional Recurrent Neural Networks for Source Identification and Forecasting of Dynamical Systems"

12 / 12 papers shown
FluidFormer: Transformer with Continuous Convolution for Particle-based Fluid Simulation
FluidFormer: Transformer with Continuous Convolution for Particle-based Fluid Simulation
Nianyi Wang
Yu Chen
Shuai Zheng
AI4CE
233
1
0
03 Aug 2025
State-space models are accurate and efficient neural operators for dynamical systems
State-space models are accurate and efficient neural operators for dynamical systems
Zheyuan Hu
Nazanin Ahmadi Daryakenari
Qianli Shen
Kenji Kawaguchi
George Karniadakis
MambaAI4CE
537
30
0
28 Jan 2025
Physics-aware deep learning framework for linear elasticity
Physics-aware deep learning framework for linear elasticity
Anisha Roy
Rikhi Bose
AI4CE
361
9
0
19 Feb 2023
Learning Point Processes using Recurrent Graph Network
Learning Point Processes using Recurrent Graph NetworkIEEE International Joint Conference on Neural Network (IJCNN), 2022
Saurabh Dash
Xueyuan She
Saibal Mukhopadhyay
GNN
142
4
0
11 Aug 2022
Physics guided neural networks for modelling of non-linear dynamics
Physics guided neural networks for modelling of non-linear dynamicsNeural Networks (NN), 2022
Haakon Robinson
Suraj Pawar
Adil Rasheed
Omer San
PINNAI4TSAI4CE
223
75
0
13 May 2022
STONet: A Neural-Operator-Driven Spatio-temporal Network
STONet: A Neural-Operator-Driven Spatio-temporal Network
Haitao Lin
Guojiang Zhao
Lirong Wu
Stan Z. Li
AI4TSAI4CE
257
1
0
18 Apr 2022
Unraveled Multilevel Transformation Networks for Predicting
  Sparsely-Observed Spatiotemporal Dynamics
Unraveled Multilevel Transformation Networks for Predicting Sparsely-Observed Spatiotemporal Dynamics
Priyabrata Saha
Saibal Mukhopadhyay
160
1
0
16 Mar 2022
Robust Hybrid Learning With Expert Augmentation
Robust Hybrid Learning With Expert Augmentation
Antoine Wehenkel
Jens Behrmann
Hsiang Hsu
Guillermo Sapiro
Gilles Louppe and
J. Jacobsen
309
15
0
08 Feb 2022
Discovering Sparse Interpretable Dynamics from Partial Observations
Discovering Sparse Interpretable Dynamics from Partial ObservationsCommunications Physics (Commun. Phys.), 2021
Peter Y. Lu
Joan Ariño Bernad
Marin Soljacic
AI4CE
213
38
0
22 Jul 2021
A Deep Learning Approach for Predicting Spatiotemporal Dynamics From
  Sparsely Observed Data
A Deep Learning Approach for Predicting Spatiotemporal Dynamics From Sparsely Observed DataIEEE Access (IEEE Access), 2020
Priyabrata Saha
Saibal Mukhopadhyay
AI4CE
279
5
0
30 Nov 2020
Augmenting Physical Models with Deep Networks for Complex Dynamics
  Forecasting
Augmenting Physical Models with Deep Networks for Complex Dynamics ForecastingInternational Conference on Learning Representations (ICLR), 2020
Yuan Yin
Vincent Le Guen
Jérémie Donà
Emmanuel de Bézenac
Ibrahim Ayed
Nicolas Thome
Patrick Gallinari
AI4CEPINN
518
164
0
09 Oct 2020
PDE-Driven Spatiotemporal Disentanglement
PDE-Driven Spatiotemporal Disentanglement
Jérémie Donà
Jean-Yves Franceschi
Sylvain Lamprier
Patrick Gallinari
OODDRL
402
31
0
04 Aug 2020
1
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