HybridNet: Integrating Model-based and Data-driven Learning to Predict
Evolution of Dynamical Systems

HybridNet: Integrating Model-based and Data-driven Learning to Predict Evolution of Dynamical Systems

19 June 2018

Saibal Mukhopadhyay

Papers citing "HybridNet: Integrating Model-based and Data-driven Learning to Predict Evolution of Dynamical Systems"

9 / 9 papers shown

Title
Hybrid Modeling Design Patterns Maja Rudolph Stefan Kurz Barbara Rakitsch AI4CE 24 7 0 29 Dec 2023
Wave Physics-informed Matrix Factorizations Harsha Vardhan Tetali J. Harley B. Haeffele 27 0 0 21 Dec 2023
Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing Salah A. Faroughi N. Pawar C. Fernandes Maziar Raissi Subasish Das N. Kalantari S. K. Mahjour PINN AI4CE 27 47 0 14 Nov 2022
When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning Chuizheng Meng Sungyong Seo Defu Cao Sam Griesemer Yan Liu PINN AI4CE 34 54 0 31 Mar 2022
Extension of Dynamic Mode Decomposition for dynamic systems with incomplete information based on t-model of optimal prediction Aleksandr Katrutsa S. Utyuzhnikov Ivan V. Oseledets 17 4 0 23 Feb 2022
A Review of Physics-based Machine Learning in Civil Engineering S. Vadyala S. N. Betgeri J. Matthews Elizabeth Matthews AI4CE 25 152 0 09 Oct 2021
Physics-Guided Deep Learning for Dynamical Systems: A Survey Rui Wang Rose Yu AI4CE PINN 31 64 0 02 Jul 2021
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling Naoya Takeishi Alexandros Kalousis DRL AI4CE 22 54 0 25 Feb 2021
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung W. Wong W. Woo 215 7,902 0 13 Jun 2015