Papers
Communities
Events
Blog
Pricing
Search
Open menu
Home
Papers
2306.13867
Cited By
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
24 June 2023
Truong X. Nghiem
Ján Drgoňa
Colin N. Jones
Zoltán Nagy
Roland Schwan
Biswadip Dey
Ankush Chakrabarty
Stefano Di Cairano
J. Paulson
Andrea Carron
Melanie Zeilinger
Wenceslao Shaw-Cortez
D. Vrabie
PINN
AI4CE
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems"
6 / 6 papers shown
Title
Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning
Jiang Chang
Deekshith Basvoju
Aleksandar Vakanski
Indrajit Charit
Min Xian
AI4CE
113
0
0
28 Jan 2025
Domain-decoupled Physics-informed Neural Networks with Closed-form Gradients for Fast Model Learning of Dynamical Systems
Henrik Krauss
Tim-Lukas Habich
Max Bartholdt
Thomas Seel
Moritz Schappler
PINN
AI4CE
95
3
0
27 Aug 2024
CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian Optimization
Wei-Ting Tang
J. Paulson
65
1
0
13 May 2024
DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems
Yair Schiff
Zhong Yi Wan
Jeffrey B. Parker
Stephan Hoyer
Volodymyr Kuleshov
Fei Sha
Leonardo Zepeda-Núñez
142
15
0
06 Feb 2024
Physics-Informed Multi-Agent Reinforcement Learning for Distributed Multi-Robot Problems
Eduardo Sebastián
T. Duong
Nikolay Atanasov
Eduardo Montijano
C. Sagüés
135
3
0
30 Dec 2023
Towards Robust and Stable Deep Learning Algorithms for Forward Backward Stochastic Differential Equations
Batuhan Güler
Alexis Laignelet
P. Parpas
OOD
147
17
0
25 Oct 2019
1