Arabius.jl：一个可微燃烧SimulationPackage
Arrhenius.jl: A Differentiable Combustion SimulationPackage
摘要：
燃烧动力学建模是燃烧模拟的一个组成部分，广泛的研究一直致力于开发高保真度和计算负担得起的模型。尽管有这些努力，建模燃烧动力学仍然是具有挑战性的，由于专家知识和优化实验的需求，以及缺乏相关的不确定性的理解。因此，近年来，数据驱动的方法，使高效的发现和校准的动力学模型受到了广泛的关注，其核心是基于大数据的优化。微分规划通过有效地计算目标函数对模型参数的梯度，是一种很有前途的从数据中学习动力学模型的方法。然而，在实践中实施差异化方案往往具有挑战性。因此，它仍然不能用于广泛使用的燃烧模拟软件包，如CHEMKIN和Cantera。在这里，我们提出了一个利用Julia中生态系统的可微分燃烧模拟包，包括用于求解微分方程的DifferentialEquations.jl，用于自动微分的ForwardDiff.jl，以及用于将神经网络模型纳入燃烧模拟并使用最先进的深度学习优化器优化神经网络模型的Flux.jl。我们证明了可微编程在高效和准确的梯度计算中的好处，以及在不确定性量化、动力学模型简化、数据同化和模型发现中的应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
