arXiv:2607. 16177v1 Announce Type: new Abstract: Reinforcement learning (RL) has recently emerged as a promising feedback control strategy for nonlinear and complex dynamical systems.
By Matteo Tomasetto, Nicol\`o Botteghi, Gabriele Bruni, Andrea Manzoni
arXiv:2607. 01528v1 Announce Type: new Abstract: Small multirotor aircraft are increasingly tasked with operations in the atmospheric boundary layer, where turbulent winds comparable to the vehicle's airspeed degrade trajectory tracking and can defeat conventional feedback control.
By Abdullah Al Tasim, Wei Sun
arXiv:2606. 24999v1 Announce Type: new Abstract: High-dimensional partial differential equations (PDEs) with unknown coefficients arise widely in scientific machine learning, including continuous-time reinforcement learning, yet solving them efficiently in a data-driven way remains challenging.
By Yanwei Jia, Du Ouyang, Huy\^en Pham, Xun Yu Zhou
arXiv:2608. 12624v1 Announce Type: new Abstract: Structure-preserving machine learning embeds physical structure directly into model architectures, yet uncertainty quantification (UQ) for such hard-constrained models remains limited because standard UQ methods may violate the encoded admissibility conditions, require architectural modifications, or impose substantial computational costs.
By Zequn He, Celia Reina
arXiv:2509. 23960v2 Announce Type: replace-cross Abstract: Co-optimizing safety and performance in large-scale multi-agent systems remains a fundamental challenge.
By Manan Tayal, Aditya Singh, Shishir Kolathaya, Somil Bansal
arXiv:2607. 16313v1 Announce Type: new Abstract: Traditional controllers are designed for specific systems and do not transfer across different system orders and dynamics.
By Klinsmann Agyei, Pouria Sarhadi