Towards Foundation Models for the Industrial Forecasting of Chemical Kinetics

TL;DR

This paper explores the use of a novel neural network architecture, MLP-Mixer, for modeling stiff chemical kinetics in industrial settings, demonstrating its potential as a foundation model for chemical reaction time-series prediction.

Contribution

It introduces the application of the MLP-Mixer architecture to chemical kinetics, showcasing its effectiveness compared to traditional numerical methods.

Findings

MLP-Mixer outperforms traditional methods on the ROBER benchmark.

The approach demonstrates potential for industrial chemical kinetics modeling.

Provides insights into foundation models for chemical time-series data.

Abstract

Scientific Machine Learning is transforming traditional engineering industries by enhancing the efficiency of existing technologies and accelerating innovation, particularly in modeling chemical reactions. Despite recent advancements, the issue of solving stiff chemically reacting problems within computational fluid dynamics remains a significant issue. In this study we propose a novel approach utilizing a multi-layer-perceptron mixer architecture (MLP-Mixer) to model the time-series of stiff chemical kinetics. We evaluate this method using the ROBER system, a benchmark model in chemical kinetics, to compare its performance with traditional numerical techniques. This study provides insight into the industrial utility of the recently developed MLP-Mixer architecture to model chemical kinetics and provides motivation for such neural architecture to be used as a base for time-series foundation models.