arXiv Machine Learning July 17, 2026 By Teruki Kato, Ryotaro Shima, Kenji Kashima

Modeling and Control of Deep Sign-Definite Dynamics with Application to Hybrid Powertrain Control

Read the original on arXiv Machine Learning →

arXiv:2509. 19869v2 Announce Type: replace-cross Abstract: Data-driven control increasingly relies on deep models for complex systems whose first-principles models are difficult to obtain.

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 Machine Learning.

arXiv Machine Learning

Jun 24

Hessian-augmented Supervised Learning for Hamilton-Jacobi-Bellman PDEs

arXiv:2606. 23827v1 Announce Type: cross Abstract: A data-driven method is developed for approximating value functions in deterministic optimal control problems with nonlinear control-affine dynamics.

By Mat\'ias G\'omez-Aedo, Behzad Azmi, Yuyang Huang, Dante Kalise, Karl Kunisch

arXiv Machine Learning

Jun 9

Differentiable Weightless Controllers: Learning Logic Circuits for Continuous Control

arXiv:2512. 01467v2 Announce Type: replace Abstract: Controlling autonomous systems under real-world conditions often requires policies that can be evaluated with low latency and minimal energy consumption.

By Fabian Kresse, Christoph H. Lampert

agents efficiency benchmarks

arXiv AI

Aug 7

Dream-MPC: Gradient-Based Model Predictive Control with Latent Imagination

arXiv:2605. 04568v3 Announce Type: replace-cross Abstract: State-of-the-art model-based Reinforcement Learning (RL) approaches either use gradient-free, population-based methods for planning, learned policy networks, or a combination of policy networks and planning.

By Jonathan Spieler, Sven Behnke

reinforcement-learning benchmarks

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

Sep 1

Generalization as a robust performance property of learning-enabled dynamical systems

arXiv:2608.30431v1 Announce Type: cross Abstract: By focusing on algorithmic stability as a means of establishing out-of-sample bounds, we provide a system-theoretic interpretation of generalization...

By Filippo Fabiani

arXiv Machine Learning

Jul 7

Model Predictive Path Integral PID Control for Learning-Based Path Following

arXiv:2603. 29499v2 Announce Type: replace-cross Abstract: Classical proportional--integral--derivative (PID) control remains widely used in industrial control systems, while model predictive control (MPC) is actively studied to achieve higher performance for systems with nonlinear dynamics.

By Teruki Kato, Koshi Oishi, Seigo Ito

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Hessian-augmented Supervised Learning for Hamilton-Jacobi-Bellman PDEs

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Dream-MPC: Gradient-Based Model Predictive Control with Latent Imagination

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