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Tag: math.DS
arXiv:2604.05042v1 Announce Type: new Abstract: Recent advances at the intersection of control theory, neuroscience, and machine learning have revealed novel mechanisms by which dynamical systems …
arXiv:2604.02601v1 Announce Type: new Abstract: Data-driven discovery of governing equations from noisy observations remains a fundamental challenge in scientific machine learning. While GENERIC formalism informed …
arXiv:2603.23854v1 Announce Type: new Abstract: Symbolic discovery of governing equations is a long-standing goal in scientific machine learning, yet a fundamental trade-off persists between interpretability …
arXiv:2603.22871v1 Announce Type: new Abstract: Current large language models (LLMs) primarily rely on linear sequence generation and massive parameter counts, yet they severely struggle with …
arXiv:2603.15927v1 Announce Type: new Abstract: We propose a data-driven framework to learn interaction kernels in stochastic multi-agent systems. Our approach aims at identifying the functional …
arXiv:2603.04420v1 Announce Type: new Abstract: Critical transitions are the abrupt shifts between qualitatively different states of a system, and they are crucial to understanding tipping …
arXiv:2602.16864v1 Announce Type: new Abstract: Time series (TS) modeling has come a long way from early statistical, mainly linear, approaches to the current trend in …
arXiv:2602.17089v1 Announce Type: new Abstract: Diffusion models recently developed for generative AI tasks can produce high-quality samples while still maintaining diversity among samples to promote …