Zobrazeno 1 - 10
of 671
pro vyhledávání: '"Crommelin, Daan"'
Publikováno v:
Computers & Fluids 285(2024)106469
We introduce a simple, stochastic, a-posteriori, turbulence closure model based on a reduced subgrid scale term. This subgrid scale term is tailor-made to capture the statistics of a small set of spatially-integrate quantities of interest (QoIs), wit
Externí odkaz:
http://arxiv.org/abs/2407.14132
Publikováno v:
J. Chem. Phys. 160, 024108 2024
Simulations of condensed matter systems often focus on the dynamics of a few distinguished components but require integrating the dynamics of the full system. A prime example is a molecular dynamics simulation of a (macro)molecule in solution, where
Externí odkaz:
http://arxiv.org/abs/2306.17672
Neural closure models have recently been proposed as a method for efficiently approximating small scales in multiscale systems with neural networks. The choice of loss function and associated training procedure has a large effect on the accuracy and
Externí odkaz:
http://arxiv.org/abs/2210.14675
Autor:
Verheul, Nick, Crommelin, Daan
Publikováno v:
Commun. Appl. Math. Comput. Sci. 16 (2021) 33-57
In this study we investigate a data-driven stochastic methodology to parameterize small-scale features in a prototype multiscale dynamical system, the Lorenz '96 (L96) model. We propose to model the small-scale features using a vector autoregressive
Externí odkaz:
http://arxiv.org/abs/2010.03293
Autor:
Crommelin, Daan, Edeling, Wouter
In simulations of multiscale dynamical systems, not all relevant processes can be resolved explicitly. Taking the effect of the unresolved processes into account is important, which introduces the need for paramerizations. We present a machine-learni
Externí odkaz:
http://arxiv.org/abs/2004.01457
A popular method to compute first-passage probabilities in continuous-time Markov chains is by numerically inverting their Laplace transforms. Past decades, the scientific computing community has developed excellent numerical methods for solving prob
Externí odkaz:
http://arxiv.org/abs/2003.14300
Publikováno v:
In Computers and Mathematics with Applications 1 August 2023 143:94-107
Publikováno v:
Chaos: An Interdisciplinary Journal of Nonlinear Science, 29(3):033131 (2019)
We develop a new algorithm for the estimation of rare event probabilities associated with the steady-state of a Markov stochastic process with continuous state space $\mathbb R^d$ and discrete time steps (i.e. a discrete-time $\mathbb R^d$-valued Mar
Externí odkaz:
http://arxiv.org/abs/1904.02966
Publikováno v:
Journal of Chemical Physics; 1/14/2024, Vol. 160 Issue 2, p1-12, 12p