Articles citing this article

The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).

Cited article:

State Dependent Riccati for dynamic boundary control to optimize irrigation in Richards’ equation framework

Alessandro Alla, Marco Berardi and Luca Saluzzi
Mathematics and Computers in Simulation 232 261 (2025)
https://doi.org/10.1016/j.matcom.2024.12.020

Numerical Realization of the Mortensen Observer via a Hessian-Augmented Polynomial Approximation of the Value Function

Tobias Breiten, Karl K. Kunisch and Jesper Schröder
SIAM Journal on Scientific Computing 47 (1) A181 (2025)
https://doi.org/10.1137/23M1613773

Sample Size Estimates for Risk-Neutral Semilinear PDE-Constrained Optimization

Johannes Milz and Michael Ulbrich
SIAM Journal on Optimization 34 (1) 844 (2024)
https://doi.org/10.1137/22M1512636

A Comparison Study of Supervised Learning Techniques for the Approximation of High Dimensional Functions and Feedback Control

Mathias Oster, Luca Saluzzi and Tizian Wenzel
Dynamic Games and Applications (2024)
https://doi.org/10.1007/s13235-024-00610-6

A Neural Network Approach for Stochastic Optimal Control

Xingjian Li, Deepanshu Verma and Lars Ruthotto
SIAM Journal on Scientific Computing 46 (5) C535 (2024)
https://doi.org/10.1137/23M155832X

Neural network approaches for parameterized optimal control

Deepanshu Verma, Nick Winovich, Lars Ruthotto and Bart van Bloemen Waanders
Foundations of Data Science (2024)
https://doi.org/10.3934/fods.2024042

Offline supervised learning v.s. online direct policy optimization: A comparative study and a unified training paradigm for neural network-based optimal feedback control

Yue Zhao and Jiequn Han
Physica D: Nonlinear Phenomena 462 134130 (2024)
https://doi.org/10.1016/j.physd.2024.134130

Deep Neural Network Approximations for the Stable Manifolds of the Hamilton–Jacobi–Bellman Equations

Guoyuan Chen
IEEE Transactions on Automatic Control 69 (10) 7239 (2024)
https://doi.org/10.1109/TAC.2024.3396107

Hermite kernel surrogates for the value function of high-dimensional nonlinear optimal control problems

Tobias Ehring and Bernard Haasdonk
Advances in Computational Mathematics 50 (3) (2024)
https://doi.org/10.1007/s10444-024-10128-5

Consistent smooth approximation of feedback laws for infinite horizon control problems with non-smooth value functions

Karl Kunisch and Donato Vásquez-Varas
Journal of Differential Equations 411 438 (2024)
https://doi.org/10.1016/j.jde.2024.08.010

BC-PINN: an adaptive physics informed neural network based on biased multiobjective coevolutionary algorithm

Zhicheng Zhu, Jia Hao, Jin Huang and Biao Huang
Neural Computing and Applications 35 (28) 21093 (2023)
https://doi.org/10.1007/s00521-023-08876-4

State-dependent Riccati equation feedback stabilization for nonlinear PDEs

Alessandro Alla, Dante Kalise and Valeria Simoncini
Advances in Computational Mathematics 49 (1) (2023)
https://doi.org/10.1007/s10444-022-09998-4

A two-stage deep-learning-based balancing method for rotating machinery

Shun Zhong, Hong-Xiang Han and Lei Hou
Measurement Science and Technology 34 (4) 045903 (2023)
https://doi.org/10.1088/1361-6501/acabdd

A Neural Network Approach for High-Dimensional Optimal Control Applied to Multiagent Path Finding

Derek Onken, Levon Nurbekyan, Xingjian Li, et al.
IEEE Transactions on Control Systems Technology 31 (1) 235 (2023)
https://doi.org/10.1109/TCST.2022.3172872

Synchronization of reaction‐diffusion neural networks with distributed delay via quantized boundary control

Chuan Zhang, Han Xiang, Xianfu Zhang, Yingxin Guo and Hao Zhang
International Journal of Adaptive Control and Signal Processing 37 (5) 1166 (2023)
https://doi.org/10.1002/acs.3567

Data-Driven Tensor Train Gradient Cross Approximation for Hamilton–Jacobi–Bellman Equations

Sergey Dolgov, Dante Kalise and Luca Saluzzi
SIAM Journal on Scientific Computing 45 (5) A2153 (2023)
https://doi.org/10.1137/22M1498401

Gradient-augmented Supervised Learning of Optimal Feedback Laws Using State-Dependent Riccati Equations

Giacomo Albi, Sara Bicego and Dante Kalise
IEEE Control Systems Letters 6 836 (2022)
https://doi.org/10.1109/LCSYS.2021.3086697

An Approximation Scheme for Distributionally Robust PDE-Constrained Optimization

Johannes Milz and Michael Ulbrich
SIAM Journal on Control and Optimization 60 (3) 1410 (2022)
https://doi.org/10.1137/20M134664X

Tensor Decomposition Methods for High-dimensional Hamilton--Jacobi--Bellman Equations

Sergey Dolgov, Dante Kalise and Karl K. Kunisch
SIAM Journal on Scientific Computing 43 (3) A1625 (2021)
https://doi.org/10.1137/19M1305136

Learning an Optimal Feedback Operator Semiglobally Stabilizing Semilinear Parabolic Equations

Karl Kunisch, Sérgio S. Rodrigues and Daniel Walter
Applied Mathematics & Optimization 84 (S1) 277 (2021)
https://doi.org/10.1007/s00245-021-09769-5