arXiv AI July 2, 2026 By Jeffrey Fang, Keyi Shen, Anutam Srinivasan, Glen Chou

GPU-Parallel Linearization Error Bounds for Real-Time Robust Optimal Control of Nonlinear and Neural Network Dynamics

arXiv:2607. 01203v1 Announce Type: cross Abstract: This paper studies real-time robust optimal control for uncertain nonlinear systems, where linear time-varying (LTV) approximations make planning tractable but require sound linearization error bounds (LEBs) to guarantee robust constraint satisfaction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 4

Certified Neural Approximations of Nonlinear Dynamics

arXiv:2505. 15497v3 Announce Type: replace Abstract: Neural networks hold great potential to act as approximate models of nonlinear dynamical systems, with the resulting neural approximations enabling verification and control of such systems.

By Frederik Baymler Mathiesen, Nikolaus Vertovec, Francesco Fabiano, Luca Laurenti, Alessandro Abate

benchmarks safety

arXiv Machine Learning

Sep 22

Scalable Incremental Robustness Analysis of Neural Network Feedback Systems

arXiv:2609.22576v1 Announce Type: cross Abstract: Semidefinite programming (SDP) certificates for feedback systems containing deep neural networks (NNs) typically scale with the total number of neuro...

By Zichen Wang, Peter Seiler, Geir Dullerud, Bin Hu

benchmarks

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 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

arXiv AI

Jun 16

Pixels to Proofs: Probabilistically-Safe Latent World Model Control via Parallel Conformal Robust MPC

arXiv:2606. 15594v1 Announce Type: cross Abstract: We present SLS^2, a framework for safe feedback motion planning from pixels using robust model predictive control (MPC) in learned latent world models.

By Devesh Nath, Anutam Srinivasan, Haoran Yin, Ruitong Jiang, Jeffrey Fang, Glen Chou

rag safety

arXiv Machine Learning

Aug 13

Forward Trajectory Steering for Hamilton-Jacobi Reachability Analysis

arXiv:2608. 11480v1 Announce Type: cross Abstract: Hamilton-Jacobi (HJ) reachability provides a mathematically rigorous framework for safe control of dynamical systems, but its practical application is bottlenecked by the computational complexity of solving Hamilton-Jacobi-Isaacs variational inequality PDEs in high dimensions.

By Sungje Park, Stephen Tu

benchmarks safety

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Certified Neural Approximations of Nonlinear Dynamics

Scalable Incremental Robustness Analysis of Neural Network Feedback Systems

Implicit Neural Networks as Static Controllers: Certificates and Performance Separation

DiffSlack: Learning under Nonlinear Inequality Constraints via Learnable Slack Variables

Pixels to Proofs: Probabilistically-Safe Latent World Model Control via Parallel Conformal Robust MPC

Forward Trajectory Steering for Hamilton-Jacobi Reachability Analysis