Towards Foundation Models for the Industrial Forecasting of Chemical Kinetics
This work addresses stiff chemical kinetics modeling for industrial forecasting in chemical engineering, representing an incremental application of an existing neural architecture to a new domain.
The authors tackled the problem of solving stiff chemically reacting problems in computational fluid dynamics by proposing a novel approach using a multi-layer-perceptron mixer architecture (MLP-Mixer) to model time-series of stiff chemical kinetics, evaluating it on the ROBER benchmark system and comparing performance with traditional numerical techniques.
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.