6 papers
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…
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…
The random timestep Euler method and its continuous dynamics
Jonas Latz
ODE solvers with randomly sampled timestep sizes appear in the context of chaotic dynamical systems, differential equations with low regularity, and, implicitly, in stochastic opti…
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…
Adaptive stepsize algorithms for Langevin dynamics
Alix Leroy, Benedict Leimkuhler, Jonas Latz +1
We discuss the design of an invariant measure-preserving transformed dynamics for the numerical treatment of Langevin dynamics based on rescaling of time, with the goal of sampling…