Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kchaou, Mahdi, Contino, Francesco, Coppitters, Diederik
Format:	Preprint
Published:	2025
Subjects:	Systems and Control Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Global Search of Optimal Spacecraft Trajectories using Amortization and Deep Generative Models
by: Beeson, Ryne, et al.
Published: (2024)

Neural Approximators for Low-Thrust Trajectory Transfer Cost and Reachability
by: Zhang, Zhong, et al.
Published: (2025)

Discrete fully probabilistic design: towards a control pipeline for the synthesis of policies from examples
by: Ferrentino, Enrico, et al.
Published: (2021)

Heat Death of Generative Models in Closed-Loop Learning
by: Marchi, Matteo, et al.
Published: (2024)

SafEDMD: A Koopman-based data-driven controller design framework for nonlinear dynamical systems
by: Strässer, Robin, et al.
Published: (2024)

Alignment of large language models with constrained learning
by: Zhang, Botong, et al.
Published: (2025)

Convergence and stability of Q-learning in Hierarchical Reinforcement Learning
by: Manenti, Massimiliano, et al.
Published: (2025)

Variance-Reduced Cascade Q-learning: Algorithms and Sample Complexity
by: Boveiri, Mohammad, et al.
Published: (2024)

Safe learning-based control via function-based uncertainty quantification
by: Tokmak, Abdullah, et al.
Published: (2026)

Stability properties of gradient flow dynamics for the symmetric low-rank matrix factorization problem
by: Mohammadi, Hesameddin, et al.
Published: (2024)

Global Search for Optimal Low Thrust Spacecraft Trajectories using Diffusion Models and the Indirect Method
by: Graebner, Jannik, et al.
Published: (2025)

Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method
by: Caldarelli, Edoardo, et al.
Published: (2024)

PolyFormer: learning efficient reformulations for scalable optimization under complex physical constraints
by: Wen, Yilin, et al.
Published: (2026)

GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systems
by: van Laatum, Bart, et al.
Published: (2024)

A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
by: Gokhale, Anand, et al.
Published: (2026)

Independent policy gradient-based reinforcement learning for economic and reliable energy management of multi-microgrid systems
by: Hu, Junkai, et al.
Published: (2025)

Certifying Stability of Reinforcement Learning Policies using Generalized Lyapunov Functions
by: Long, Kehan, et al.
Published: (2025)

Diffusion Policies for Generative Modeling of Spacecraft Trajectories
by: Briden, Julia, et al.
Published: (2025)

On Building Myopic MPC Policies using Supervised Learning
by: Orrico, Christopher A., et al.
Published: (2024)

Scalable spectral representations for multi-agent reinforcement learning in network MDPs
by: Ren, Zhaolin, et al.
Published: (2024)

Identifying Systems with Symmetries using Equivariant Autoregressive Reservoir Computers
by: Vides, Fredy, et al.
Published: (2023)

Dynamic financial processes identification using sparse regressive reservoir computers
by: Vides, Fredy, et al.
Published: (2023)

Exploiting inter-agent coupling information for efficient reinforcement learning of cooperative LQR
by: Syed, Shahbaz P Qadri, et al.
Published: (2025)

Stochastic Approximation with Unbounded Markovian Noise: A General-Purpose Theorem
by: Haque, Shaan Ul, et al.
Published: (2024)

A Complete Set of Quadratic Constraints for Repeated ReLU and Generalizations
by: Noori, Sahel Vahedi, et al.
Published: (2024)

An active learning method for solving competitive multi-agent decision-making and control problems
by: Fabiani, Filippo, et al.
Published: (2022)

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

Discrete-time Contraction-based Control of Nonlinear Systems with Parametric Uncertainties using Neural Networks
by: Wei, Lai, et al.
Published: (2021)

Compositional Diffusion Models for Powered Descent Trajectory Generation with Flexible Constraints
by: Briden, Julia, et al.
Published: (2024)

Mutual Information Optimal Density Control of Linear Systems and Generalized Schrödinger Bridges with Reference Refinement
by: Enami, Shoju, et al.
Published: (2026)

Differentiable Model Predictive Control on the GPU
by: Adabag, Emre, et al.
Published: (2025)

Learning Exactly Linearizable Deep Dynamics Models
by: Moriyasu, Ryuta, et al.
Published: (2023)

Operator Models for Continuous-Time Offline Reinforcement Learning
by: Hoischen, Nicolas, et al.
Published: (2025)

Model approximation in MDPs with unbounded per-step cost
by: Bozkurt, Berk, et al.
Published: (2024)

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

Physics-informed Gaussian Processes as Linear Model Predictive Controller
by: Tebbe, Jörn, et al.
Published: (2024)

Tempering the Bayes Filter towards Improved Model-Based Estimation
by: van Zutphen, Menno, et al.
Published: (2025)

Model-Free Output Feedback Stabilization via Policy Gradient Methods
by: Zhang, Ankang, et al.
Published: (2026)

Incremental Correction in Dynamic Systems Modelled with Neural Networks for Constraint Satisfaction
by: Cho, Namhoon, et al.
Published: (2022)

ReLU Networks for Model Predictive Control: Network Complexity and Performance Guarantees
by: Li, Xingchen, et al.
Published: (2026)