Image de couverture de livre

Equation-Free Coarse Control of Distributed Parameter Systems via Local Neural Operators

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Fabiani, Gianluca, Siettos, Constantinos, Kevrekidis, Ioannis G.
Format:	Preprint
Publié:	2025
Sujets:	Systems and Control Machine Learning Numerical Analysis Optimization and Control 93B52, 93C20, 47N70, 65J15, 65M32, 68T07, 68T20, 68T05,
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

RandONet: Shallow-Networks with Random Projections for learning linear and nonlinear operators
par: Fabiani, Gianluca, et autres
Publié: (2024)

Duality and DeepMartingale for High-Dimensional Optimal Switching: Computable Upper Bounds and Approximation-Expressivity Guarantees
par: Ye, Junyan, et autres
Publié: (2026)

Recurrent Neural Operators: Stable Long-Term PDE Prediction
par: Ye, Zaijun, et autres
Publié: (2025)

Deep Relaxation of Controlled Stochastic Gradient Descent via Singular Perturbations
par: Bardi, Martino, et autres
Publié: (2022)

Neural Network Convergence for Variational Inequalities
par: Zhao, Yun, et autres
Publié: (2025)

Nonlinear Discrete-Time Observers with Physics-Informed Neural Networks
par: Alvarez, Hector Vargas, et autres
Publié: (2024)

Deep neural network yields regularization for ill-posed inverse problems
par: Zhu, Qiao, et autres
Publié: (2026)

Data-driven Control of Agent-based Models: an Equation/Variable-free Machine Learning Approach
par: Patsatzis, Dimitrios G., et autres
Publié: (2022)

Linear Stability Analysis of Physics-Informed Random Projection Neural Networks for ODEs
par: Fabiani, Gianluca, et autres
Publié: (2024)

Solving the BGK Model and Boltzmann equation by Fourier Neural Operator with conservative constraints
par: Hu, Boyun, et autres
Publié: (2025)

A Unified Matrix-Spectral Framework for Stability and Interpretability in Deep Learning
par: Katende, Ronald
Publié: (2026)

Physics-informed approach for exploratory Hamilton--Jacobi--Bellman equations via policy iterations
par: Kim, Yeongjong, et autres
Publié: (2025)

Dynamics-Encoded Deep Learning for Robust System Identification and Parameter Estimation
par: Ho, Caitlin, et autres
Publié: (2024)

Neural Actor-Critic Methods for Hamilton-Jacobi-Bellman PDEs: Asymptotic Analysis and Numerical Studies
par: Cohen, Samuel N., et autres
Publié: (2025)

Neural Policy Iteration for Stochastic Optimal Control: A Physics-Informed Approach
par: Kim, Yeongjong, et autres
Publié: (2025)

Data-Free Asymptotics-Informed Operator Networks for Singularly Perturbed PDEs
par: Lee, Jinsil, et autres
Publié: (2025)

Separable Approximations of Optimal Value Functions and Their Representation by Neural Networks
par: Sperl, Mario, et autres
Publié: (2025)

Quantifying and Improving the Accuracy of Electromagnetic Transient-Transient Stability Hybrid Simulation
par: Wang, Bin, et autres
Publié: (2026)

A deep solver for BSDEs with jumps
par: Andersson, Kristoffer, et autres
Publié: (2022)

Finite Element Analysis for the Chafee-Infante Equation Using Distributed Feedback Control
par: Singh, Shishu Pal, et autres
Publié: (2025)

A Unified Framework for Lifted Training and Inversion Approaches
par: Wang, Xiaoyu, et autres
Publié: (2025)

Fredholm Neural Networks
par: Georgiou, Kyriakos, et autres
Publié: (2024)

Hybrid Least Squares/Gradient Descent Methods for DeepONets
par: Choi, Jun, et autres
Publié: (2025)

Fractional-Boundary-Regularized Deep Galerkin Method for Variational Inequalities in Mixed Optimal Stopping and Control
par: Zhao, Yun, et autres
Publié: (2025)

Sampling Distributions as Regularization in Learned Inverse Problems
par: Babyale, Sandra R., et autres
Publié: (2026)

Control randomisation approach for policy gradient and application to reinforcement learning in optimal switching
par: Denkert, Robert, et autres
Publié: (2024)

Smart leverage? Rethinking the role of Leveraged Exchange Traded Funds in constructing portfolios to beat a benchmark
par: van Staden, Pieter, et autres
Publié: (2024)

GoRINNs: Godunov-Riemann Informed Neural Networks for Learning Hyperbolic Conservation Laws
par: Patsatzis, Dimitrios G., et autres
Publié: (2024)

Quantization Robustness of Monotone Operator Equilibrium Networks
par: Li, James, et autres
Publié: (2026)

Model-Free DRL Control for Power Inverters: From Policy Learning to Real-Time Implementation via Knowledge Distillation
par: Yang, Yang, et autres
Publié: (2026)

Development and Analysis of Chien-Physics-Informed Neural Networks for Singular Perturbation Problems
par: Singh, Gautam, et autres
Publié: (2025)

Enabling Local Neural Operators to perform Equation-Free System-Level Analysis
par: Fabiani, Gianluca, et autres
Publié: (2025)

Symbolic recovery of PDEs from measurement data
par: Morina, Erion, et autres
Publié: (2026)

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems
par: Song, Yongcun, et autres
Publié: (2026)

Neural Network Element Method for Partial Differential Equations
par: Wang, Yifan, et autres
Publié: (2025)

Learning Contractive Integral Operators with Fredholm Integral Neural Operators
par: Georgiou, Kyriakos C., et autres
Publié: (2026)

Deep Quadratic Hedging
par: Gnoatto, Alessandro, et autres
Publié: (2022)

RONOM: Reduced-Order Neural Operator Modeling
par: Dummer, Sven, et autres
Publié: (2025)

EGPT-PINN: Entropy-enhanced Generative Pre-Trained Physics Informed Neural Networks for parameterized nonlinear conservation laws
par: Ji, Yajie, et autres
Publié: (2025)

GAN-based iterative motion estimation in HASTE MRI
par: Feinler, Mathias S., et autres
Publié: (2024)