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pro vyhledávání: '"Rielly, Victor"'
Learning a nonparametric system of ordinary differential equations (ODEs) from $n$ trajectory snapshots in a $d$-dimensional state space requires learning $d$ functions of $d$ variables. Explicit formulations scale quadratically in $d$ unless additio
Externí odkaz:
http://arxiv.org/abs/2306.10189
Learning nonparametric systems of Ordinary Differential Equations (ODEs) dot x = f(t,x) from noisy data is an emerging machine learning topic. We use the well-developed theory of Reproducing Kernel Hilbert Spaces (RKHS) to define candidates for f for
Externí odkaz:
http://arxiv.org/abs/2206.15215
Publikováno v:
In Journal of Computational Physics 15 June 2024 507
Consider a surface $S$ and let $M\subset S$. If $S\setminus M$ is not connected, then we say $M$ \emph{separates} $S$, and we refer to $M$ as a \emph{separating set} of $S$. If $M$ separates $S$, and no proper subset of $M$ separates $S$, then we say
Externí odkaz:
http://arxiv.org/abs/1701.04496