arXiv:2603.29050v1 Announce Type: cross
Abstract: Foot slip is a major source of instability in bipedal locomotion on low-friction or uncertain terrain. Standard control approaches typically assume no-slip contact and therefore degrade when slip occur…
arXiv:2603.29708v1 Announce Type: new
Abstract: Robots operating in human-centric environments must be both robust to disturbances and provably safe from collisions. Achieving these properties simultaneously and efficiently remains a central challenge…
arXiv:2603.28965v1 Announce Type: cross
Abstract: This work presents a hybrid physics-informed and data-driven modeling framework for predictive control of autonomous off-road vehicles operating on deformable terrain. Traditional high-fidelity terrame…
arXiv:2603.27395v1 Announce Type: cross
Abstract: We propose a topological framework for the detection of Hopf bifurcations directly from time series, based on persistent homology applied to phase space reconstructions via Takens embedding within the …
arXiv:2603.27880v1 Announce Type: cross
Abstract: We propose a variational framework in which the kernel function k : X x X -> R, interpreted as the foundational object encoding what distinctions an agent can represent, is treated as a dynamical varia…
arXiv:2502.00472v3 Announce Type: replace
Abstract: Forecasting multiscale chaotic dynamical systems, such as turbulent flows, with deep learning remains a formidable challenge due to the spectral bias of neural networks, which hinders the accurate re…
arXiv:2603.28591v1 Announce Type: cross
Abstract: We analyze the universal approximation constraints of narrow Residual Neural Networks (ResNets) both theoretically and numerically. For deep neural networks without input space augmentation, a central …
arXiv:2603.27880v1 Announce Type: new
Abstract: We propose a variational framework in which the kernel function k : X x X -> R, interpreted as the foundational object encoding what distinctions an agent can represent, is treated as a dynamical variabl…
arXiv:2603.28074v1 Announce Type: new
Abstract: Training reinforcement learning (RL) agents to control fluid dynamics systems is computationally expensive due to the high cost of direct numerical simulations (DNS) of the governing equations. Surrogate…
arXiv:2603.26921v1 Announce Type: cross
Abstract: Physics-Informed Neural Networks (PINNs) and Neural Ordinary Differential Equations (NODEs) represent two distinct machine learning frameworks for modeling nonlinear neuronal dynamics. This study syste…