Image de couverture de livre

Revealing design archetypes and flexibility in e-molecule import pathways using Modeling to Generate Alternatives and interpretable machine learning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Kchaou, Mahdi, Contino, Francesco, Coppitters, Diederik
Format:	Preprint
Publié:	2025
Sujets:	Systems and Control Machine Learning Optimization and Control
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Global Search of Optimal Spacecraft Trajectories using Amortization and Deep Generative Models
par: Beeson, Ryne, et autres
Publié: (2024)

Neural Approximators for Low-Thrust Trajectory Transfer Cost and Reachability
par: Zhang, Zhong, et autres
Publié: (2025)

Discrete fully probabilistic design: towards a control pipeline for the synthesis of policies from examples
par: Ferrentino, Enrico, et autres
Publié: (2021)

Heat Death of Generative Models in Closed-Loop Learning
par: Marchi, Matteo, et autres
Publié: (2024)

SafEDMD: A Koopman-based data-driven controller design framework for nonlinear dynamical systems
par: Strässer, Robin, et autres
Publié: (2024)

Alignment of large language models with constrained learning
par: Zhang, Botong, et autres
Publié: (2025)

Convergence and stability of Q-learning in Hierarchical Reinforcement Learning
par: Manenti, Massimiliano, et autres
Publié: (2025)

Variance-Reduced Cascade Q-learning: Algorithms and Sample Complexity
par: Boveiri, Mohammad, et autres
Publié: (2024)

Safe learning-based control via function-based uncertainty quantification
par: Tokmak, Abdullah, et autres
Publié: (2026)

Stability properties of gradient flow dynamics for the symmetric low-rank matrix factorization problem
par: Mohammadi, Hesameddin, et autres
Publié: (2024)

Global Search for Optimal Low Thrust Spacecraft Trajectories using Diffusion Models and the Indirect Method
par: Graebner, Jannik, et autres
Publié: (2025)

Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method
par: Caldarelli, Edoardo, et autres
Publié: (2024)

PolyFormer: learning efficient reformulations for scalable optimization under complex physical constraints
par: Wen, Yilin, et autres
Publié: (2026)

GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systems
par: van Laatum, Bart, et autres
Publié: (2024)

A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
par: Gokhale, Anand, et autres
Publié: (2026)

Independent policy gradient-based reinforcement learning for economic and reliable energy management of multi-microgrid systems
par: Hu, Junkai, et autres
Publié: (2025)

Certifying Stability of Reinforcement Learning Policies using Generalized Lyapunov Functions
par: Long, Kehan, et autres
Publié: (2025)

Diffusion Policies for Generative Modeling of Spacecraft Trajectories
par: Briden, Julia, et autres
Publié: (2025)

On Building Myopic MPC Policies using Supervised Learning
par: Orrico, Christopher A., et autres
Publié: (2024)

Scalable spectral representations for multi-agent reinforcement learning in network MDPs
par: Ren, Zhaolin, et autres
Publié: (2024)

Identifying Systems with Symmetries using Equivariant Autoregressive Reservoir Computers
par: Vides, Fredy, et autres
Publié: (2023)

Dynamic financial processes identification using sparse regressive reservoir computers
par: Vides, Fredy, et autres
Publié: (2023)

Exploiting inter-agent coupling information for efficient reinforcement learning of cooperative LQR
par: Syed, Shahbaz P Qadri, et autres
Publié: (2025)

Stochastic Approximation with Unbounded Markovian Noise: A General-Purpose Theorem
par: Haque, Shaan Ul, et autres
Publié: (2024)

A Complete Set of Quadratic Constraints for Repeated ReLU and Generalizations
par: Noori, Sahel Vahedi, et autres
Publié: (2024)

An active learning method for solving competitive multi-agent decision-making and control problems
par: Fabiani, Filippo, et autres
Publié: (2022)

Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study
par: Schulze, Jan C., et autres
Publié: (2024)

Discrete-time Contraction-based Control of Nonlinear Systems with Parametric Uncertainties using Neural Networks
par: Wei, Lai, et autres
Publié: (2021)

Compositional Diffusion Models for Powered Descent Trajectory Generation with Flexible Constraints
par: Briden, Julia, et autres
Publié: (2024)

Mutual Information Optimal Density Control of Linear Systems and Generalized Schrödinger Bridges with Reference Refinement
par: Enami, Shoju, et autres
Publié: (2026)

Differentiable Model Predictive Control on the GPU
par: Adabag, Emre, et autres
Publié: (2025)

Learning Exactly Linearizable Deep Dynamics Models
par: Moriyasu, Ryuta, et autres
Publié: (2023)

Operator Models for Continuous-Time Offline Reinforcement Learning
par: Hoischen, Nicolas, et autres
Publié: (2025)

Model approximation in MDPs with unbounded per-step cost
par: Bozkurt, Berk, et autres
Publié: (2024)

Gradient Estimation and Variance Reduction in Stochastic and Deterministic Models
par: Keane, Ronan
Publié: (2024)

Physics-informed Gaussian Processes as Linear Model Predictive Controller
par: Tebbe, Jörn, et autres
Publié: (2024)

Tempering the Bayes Filter towards Improved Model-Based Estimation
par: van Zutphen, Menno, et autres
Publié: (2025)

Model-Free Output Feedback Stabilization via Policy Gradient Methods
par: Zhang, Ankang, et autres
Publié: (2026)

Incremental Correction in Dynamic Systems Modelled with Neural Networks for Constraint Satisfaction
par: Cho, Namhoon, et autres
Publié: (2022)

ReLU Networks for Model Predictive Control: Network Complexity and Performance Guarantees
par: Li, Xingchen, et autres
Publié: (2026)