arXiv Machine Learning June 2, 2026 By Neelay Junnarkar, Murat Arcak, Peter Seiler

Synthesizing Neural Network Controllers with Closed-Loop Dissipativity Guarantees

Read the original on arXiv Machine Learning →

arXiv:2404. 07373v2 Announce Type: replace-cross Abstract: This paper presents a method to synthesize neural network controllers to maximize reward subject to the hard constraint that the feedback system of plant and controller be dissipative, certifying requirements such as stability and $L_2$ gain bounds.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 14

Implicit Neural Networks as Static Controllers: Certificates and Performance Separation

arXiv:2607. 11122v1 Announce Type: cross Abstract: Implicit neural controllers (INCs) are static feedback laws that are evaluated through an algebraic fixed point {equation}; they include as special cases neural network controllers.

By Giuseppe C. Calafiore, Laurent El Ghaoui

arXiv Machine Learning

Jun 2

React to Surprises: Stable-by-Design Neural Feedback Control and the Youla-REN

arXiv:2506. 01226v3 Announce Type: replace-cross Abstract: We study parameterizations of stabilizing nonlinear policies for learning-based control.

By Nicholas H. Barbara, Ruigang Wang, Alexandre Megretski, Ian R. Manchester

arXiv Machine Learning

Jun 16

Dual-Network PINNs for Optimal Control: A Reproducible Benchmark on the Mass-Spring-Damper System

arXiv:2606. 15271v1 Announce Type: cross Abstract: This work presents a transparent and reproducible benchmark study of a direct dual-network Physics-Informed Neural Network (PINN) formulation for the optimal control of a mass-spring-damper system.

By Abdeladhim Tahimi, Rinaldo Vieira da Silva Junior

benchmarks

arXiv Machine Learning

Jun 25

Certified Robust Invariant Polytope Training in Neural Controlled ODEs

arXiv:2408. 01273v3 Announce Type: replace Abstract: We propose a framework for training neural network controllers with certified robust forward invariant polytopes.

By Akash Harapanahalli, Samuel Coogan

rag benchmarks

arXiv Machine Learning

Jun 10

PL-KKT-hPINN: Enforcing Nonlinear Equality Constraints on Neural Networks via Piecewise-Linear Projection

arXiv:2606. 10682v1 Announce Type: new Abstract: While physics-informed neural networks (PINNs) have shown strong potential for process modeling, physical equations are only enforced as soft constraints during training, and thus, they do not guarantee constraint satisfaction at inference.

By Fateme Mohammad Mohammadi, Hector Budman, Joshua L. Pulsipher

arXiv Machine Learning

Jun 5

DiffSlack: Learning under Nonlinear Inequality Constraints via Learnable Slack Variables

arXiv:2606. 05247v1 Announce Type: new Abstract: Enforcing nonlinear inequality constraints in neural networks remains challenging, especially when the output is subject to many coupled constraints.

By Ziqian Wang, Chenxi Fang, Zhen Zhang

rag benchmarks

Related stories

Implicit Neural Networks as Static Controllers: Certificates and Performance Separation

React to Surprises: Stable-by-Design Neural Feedback Control and the Youla-REN

Dual-Network PINNs for Optimal Control: A Reproducible Benchmark on the Mass-Spring-Damper System

Certified Robust Invariant Polytope Training in Neural Controlled ODEs

PL-KKT-hPINN: Enforcing Nonlinear Equality Constraints on Neural Networks via Piecewise-Linear Projection

DiffSlack: Learning under Nonlinear Inequality Constraints via Learnable Slack Variables