We discuss applications of a recently developed method for model reduction based on linear response theory of weakly coupled dynamical systems. We apply the weak coupling method to simple stochastic differential equations with slow and fast degrees of freedom. The weak coupling model reduction method results in general in a non-Markovian system; we therefore discuss the Markovianization of the system to allow for straightforward numerical integration. We compare the applied method to the equations obtained through homogenization in the limit of large timescale separation between slow and fast degrees of freedom. We numerically compare the ensemble spread from a fixed initial condition, correlation functions and exit times from a domain. The...
In this study we investigate a data-driven stochastic methodology to parametrize small-scale feature...
This paper presents a new method for the solution of multiscale stochastic differential equations at...
A new framework is proposed for the evaluation of stochastic subgrid-scale parameterizations in the...
We discuss applications of a recently developed method for model reduction based on linear response ...
We discuss applications of a recently developed method for model reduction based on linear response ...
Multiscale dynamics are frequently present in real-world processes, such as the atmosphere-ocean and...
We propose a stochastic model reduction strategy for deterministic and stochastic slow-fast systems ...
In this paper we study coupled fast-slow ordinary differential equations (ODEs) with small time scal...
Providing efficient and accurate parameterizations for model reduction is a key goal in many areas o...
In this study, we investigate how to use sample data, generated by a fully resolved multiscale model...
A deterministic multiscale toy model is studied in which a chaotic fast subsystem triggers rare tran...
A deterministic multiscale toy model is studied in which a chaotic fast subsystem triggers rare tran...
International audienceProviding efficient and accurate parameterizations for model reduction is a ke...
Abstract. A stochastic mode reduction strategy is applied to multiscale models with a de-terministic...
This work studies a two-time-scale functional system given by two jump-diffusions under the scale se...
In this study we investigate a data-driven stochastic methodology to parametrize small-scale feature...
This paper presents a new method for the solution of multiscale stochastic differential equations at...
A new framework is proposed for the evaluation of stochastic subgrid-scale parameterizations in the...
We discuss applications of a recently developed method for model reduction based on linear response ...
We discuss applications of a recently developed method for model reduction based on linear response ...
Multiscale dynamics are frequently present in real-world processes, such as the atmosphere-ocean and...
We propose a stochastic model reduction strategy for deterministic and stochastic slow-fast systems ...
In this paper we study coupled fast-slow ordinary differential equations (ODEs) with small time scal...
Providing efficient and accurate parameterizations for model reduction is a key goal in many areas o...
In this study, we investigate how to use sample data, generated by a fully resolved multiscale model...
A deterministic multiscale toy model is studied in which a chaotic fast subsystem triggers rare tran...
A deterministic multiscale toy model is studied in which a chaotic fast subsystem triggers rare tran...
International audienceProviding efficient and accurate parameterizations for model reduction is a ke...
Abstract. A stochastic mode reduction strategy is applied to multiscale models with a de-terministic...
This work studies a two-time-scale functional system given by two jump-diffusions under the scale se...
In this study we investigate a data-driven stochastic methodology to parametrize small-scale feature...
This paper presents a new method for the solution of multiscale stochastic differential equations at...
A new framework is proposed for the evaluation of stochastic subgrid-scale parameterizations in the...