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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
M. Zeilinger
Wenceslao Shaw-Cortez
D. Vrabie
PINN
AI4CE
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Papers citing
"Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems"
17 / 17 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
27
0
0
28 Jan 2025
Building Hybrid B-Spline And Neural Network Operators
Raffaele Romagnoli
Jasmine Ratchford
Mark H. Klein
AI4CE
24
1
0
06 Jun 2024
Port-Hamiltonian Neural ODE Networks on Lie Groups For Robot Dynamics Learning and Control
T. Duong
Abdullah Altawaitan
Jason Stanley
Nikolay A. Atanasov
17
9
0
17 Jan 2024
Physics-Informed Multi-Agent Reinforcement Learning for Distributed Multi-Robot Problems
Eduardo Sebastián
T. Duong
Nikolay A. Atanasov
Eduardo Montijano
C. Sagüés
16
2
0
30 Dec 2023
Learning Dissipative Neural Dynamical Systems
Yuezhu Xu
S. Sivaranjani
13
2
0
27 Sep 2023
Differentiable Safe Controller Design through Control Barrier Functions
Shuo Yang
Shaoru Chen
V. Preciado
Rahul Mangharam
33
18
0
20 Sep 2022
Structural Inference of Networked Dynamical Systems with Universal Differential Equations
James Koch
Zhao Chen
Aaron Tuor
Ján Drgoňa
D. Vrabie
PINN
18
10
0
11 Jul 2022
Neural Lyapunov Differentiable Predictive Control
Sayak Mukherjee
Ján Drgoňa
Aaron Tuor
M. Halappanavar
D. Vrabie
27
12
0
22 May 2022
Physically Consistent Neural Networks for building thermal modeling: theory and analysis
L. D. Natale
B. Svetozarevic
Philipp Heer
Colin N. Jones
PINN
AI4CE
35
83
0
06 Dec 2021
VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints
Wenjie Xu
Colin N. Jones
B. Svetozarevic
C. Laughman
Ankush Chakrabarty
13
25
0
14 Oct 2021
A Theoretical Overview of Neural Contraction Metrics for Learning-based Control with Guaranteed Stability
Hiroyasu Tsukamoto
Soon-Jo Chung
Jean-Jacques E. Slotine
Chuchu Fan
11
10
0
02 Oct 2021
Multiple shooting for training neural differential equations on time series
Evren Mert Turan
J. Jäschke
AI4TS
18
23
0
14 Sep 2021
Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions
Charles Dawson
Zengyi Qin
Sicun Gao
Chuchu Fan
102
168
0
14 Sep 2021
Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models
Yaofeng Desmond Zhong
Biswadip Dey
Amit Chakraborty
50
34
0
12 Feb 2021
Constrained Block Nonlinear Neural Dynamical Models
Elliott Skomski
Soumya Vasisht
Colby Wight
Aaron Tuor
Ján Drgoňa
D. Vrabie
AI4CE
18
15
0
06 Jan 2021
Lagrangian Neural Networks
M. Cranmer
S. Greydanus
Stephan Hoyer
Peter W. Battaglia
D. Spergel
S. Ho
PINN
121
419
0
10 Mar 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
237
11,568
0
09 Mar 2017
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