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

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

14 April 2020

Priyabrata Saha

Saibal Mukhopadhyay

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

8 / 8 papers shown

Title
State-space models are accurate and efficient neural operators for dynamical systems Zheyuan Hu Nazanin Ahmadi Daryakenari Qianli Shen Kenji Kawaguchi George Karniadakis Mamba AI4CE 75 13 0 28 Jan 2025
Physics guided neural networks for modelling of non-linear dynamics Haakon Robinson Suraj Pawar Adil Rasheed Omer San PINN AI4TS AI4CE 24 47 0 13 May 2022
STONet: A Neural-Operator-Driven Spatio-temporal Network Haitao Lin Guojiang Zhao Lirong Wu Stan Z. Li AI4TS AI4CE 21 1 0 18 Apr 2022
Robust Hybrid Learning With Expert Augmentation Antoine Wehenkel Jens Behrmann Hsiang Hsu Guillermo Sapiro Gilles Louppe and J. Jacobsen 28 8 0 08 Feb 2022
A Deep Learning Approach for Predicting Spatiotemporal Dynamics From Sparsely Observed Data Priyabrata Saha Saibal Mukhopadhyay AI4CE 23 4 0 30 Nov 2020
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting Yuan Yin Vincent Le Guen Jérémie Donà Emmanuel de Bézenac Ibrahim Ayed Nicolas Thome Patrick Gallinari AI4CE PINN 33 132 0 09 Oct 2020
Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex Q. Liao T. Poggio 213 255 0 13 Apr 2016
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung W. Wong W. Woo 239 7,916 0 13 Jun 2015