DeepAI AI Chat
Log In Sign Up

Multiscale global sensitivity analysis for stochastic chemical systems

03/17/2020
by   Michael Merritt, et al.
0

Sensitivity analysis is routinely performed on simplified surrogate models as the cost of such analysis on the original model may be prohibitive. Little is known in general about the induced bias on the sensitivity results. Within the framework of chemical kinetics, we provide a full justification of the above approach in the case of variance based methods provided the surrogate model results from the original one through the thermodynamic limit. We also provide illustrative numerical examples in context of a Michaelis–Menten system and a biochemical reaction network describing a genetic oscillator.

READ FULL TEXT

page 1

page 2

page 3

page 4

01/14/2022

Extreme learning machines for variance-based global sensitivity analysis

Variance-based global sensitivity analysis (GSA) can provide a wealth of...
08/11/2022

Surrogate-based global sensitivity analysis with statistical guarantees via floodgate

Computational models are utilized in many scientific domains to simulate...
01/15/2021

Sensitivity Prewarping for Local Surrogate Modeling

In the continual effort to improve product quality and decrease operatio...
06/16/2018

Sensitivity-driven adaptive construction of reduced-space surrogates

We develop a systematic approach for surrogate model construction in red...
03/28/2022

Surrogate Assisted Evolutionary Multi-objective Optimisation applied to a Pressure Swing Adsorption system

Chemical plant design and optimisation have proven challenging due to th...
01/17/2021

Data-driven discovery of multiscale chemical reactions governed by the law of mass action

In this paper, we propose a data-driven method to discover multiscale ch...
02/06/2019

Toward computing sensitivities of average quantities in turbulent flows

Chaotic dynamical systems such as turbulent flows are characterized by a...