Arrhenius.jl: A Differentiable Combustion SimulationPackage

TL;DR

This paper introduces Arrhenius.jl, a Julia-based differentiable combustion simulation package that enables efficient gradient computation for kinetic modeling, uncertainty quantification, and data-driven model discovery.

Contribution

It presents a novel, user-friendly differentiable combustion simulation package in Julia, integrating existing tools for advanced kinetic modeling and optimization.

Findings

Enables efficient gradient computation for combustion models.

Facilitates uncertainty quantification and model reduction.

Supports data assimilation and neural network integration.

Abstract

Combustion kinetic modeling is an integral part of combustion simulation, and extensive studies have been devoted to developing both high fidelity and computationally affordable models. Despite these efforts, modeling combustion kinetics is still challenging due to the demand for expert knowledge and optimization against experiments, as well as the lack of understanding of the associated uncertainties. Therefore, data-driven approaches that enable efficient discovery and calibration of kinetic models have received much attention in recent years, the core of which is the optimization based on big data. Differentiable programming is a promising approach for learning kinetic models from data by efficiently computing the gradient of objective functions to model parameters. However, it is often challenging to implement differentiable programming in practice. Therefore, it is still not available in widely utilized combustion simulation packages such as CHEMKIN and Cantera. Here, we present a differentiable combustion simulation package leveraging the eco-system in Julia, including DifferentialEquations.jl for solving differential equations, ForwardDiff.jl for auto-differentiation, and Flux.jl for incorporating neural network models into combustion simulations and optimizing neural network models using the state-of-the-art deep learning optimizers. We demonstrate the benefits of differentiable programming in efficient and accurate gradient computations, with applications in uncertainty quantification, kinetic model reduction, data assimilation, and model discovery.