4 papers
Data-driven approximation of Koopman operators and generators: Convergence rates and error bounds
Liam Llamazares-Elias, Samir Llamazares-Elias, Jonas Latz +1
Global information about dynamical systems can be extracted by analysing associated infinite-dimensional transfer operators, such as Perron--Frobenius and Koopman operators as well…
A parameterization of anisotropic Gaussian fields with penalized complexity priors
Liam Llamazares-Elias, Jonas Latz, Finn Lindgren
Gaussian random fields (GFs) are fundamental tools in spatial modeling and can be represented flexibly and efficiently as solutions to stochastic partial differential equations (SP…
Data-driven discovery of chemical reaction networks
Abraham Reyes-Velazquez, Stefan Güttel, Igor Larrosa +1
We propose a unified framework that allows for the full mechanistic reconstruction of chemical reaction networks (CRNs) from concentration data. The framework utilizes an integral…
Discrete-to-continuum limits of semilinear stochastic evolution equations in Banach spaces
Yves van Gennip, Jonas Latz, Joshua Willems
We study the convergence of semilinear parabolic stochastic evolution equations, posed on a sequence of Banach spaces approximating a limiting space and driven by additive white no…