Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing

TL;DR

This paper develops a physics-informed machine learning framework using support vector regression to create reduced-order models that efficiently predict reactive-mixing quantities, significantly reducing computational costs compared to high-fidelity simulations.

Contribution

The paper introduces a novel physics-informed ML approach combining finite element data generation, feature importance evaluation, and SVR-based ROMs for reactive-transport modeling.

Findings

SVR-ROMs accurately capture trends in QoI scaling laws.

The proposed models are approximately 10^7 times faster than high-fidelity simulations.

Feature importance analysis reveals key parameters influencing reactive-mixing behavior.

Abstract

This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest (QoIs) based on high-fidelity numerical simulations. QoIs include species decay, product yield, and degree of mixing. The ROMs for QoIs are applied to quantify and understand how the chemical species evolve over time. First, high-resolution datasets for constructing ROMs are generated by solving anisotropic reaction-diffusion equations using a non-negative finite element formulation for different input parameters. Non-negative finite element formulation ensures that the species concentration is non-negative (which is needed for computing QoIs) on coarse computational grids even under high anisotropy. The reactive-mixing model input parameters are a time-scale associated with flipping of velocity, a spatial-scale controlling small/large vortex structures of velocity, a perturbation parameter of the vortex-based velocity, anisotropic dispersion strength/contrast, and molecular diffusion. Second, random forests, F-test, and mutual information criterion are used to evaluate the importance of model inputs/features with respect to QoIs. Third, Support Vector Machines (SVM) and Support Vector Regression (SVR) are used to construct ROMs based on the model inputs. Then, SVR-ROMs are used to predict scaling of QoIs. Qualitatively, SVR-ROMs are able to describe the trends observed in the scaling law associated with QoIs. Fourth, the scaling law's exponent dependence on model inputs/features are evaluated using $k$-means clustering. Finally, in terms of the computational cost, the proposed SVM-ROMs and SVR-ROMs are $\mathcal{O}(10^7)$ times faster than running a high-fidelity numerical simulation for evaluating QoIs.