Abstract The multiscale modelling of geochemical processes requires efficient couplings between scales and physics. The use of machine learning techniques and neural networks has the potential to systematically improve the accuracy of models at acceptable computational costs. In this paper, we discuss an efficient framework to transfer information between multi-physics models across spatial scales. In the first example, we train a shallow neural network based on the results of microscopic geochemical reactive transport simulations, and integrate it in a Darcy-scale reactive transport code. In the second example, we train a neural network on geochemical speciation data produced from dedicated geochemical solvers, and adapted to the needs of a lab-on-a-chip microfluidic experiment, in order to accelerate the geochemical calculations. The reactive transport simulation benchmarks show that the neural network approach performs better than the full speciation reactive transport simulations or the look up table-based approaches, both in terms of computational efficiency and memory requirements. Based on these results we discuss the advantages and drawbacks of each simulation approach as well as the potential for further development of the modelling algorithms.

中文翻译：

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反应传输模拟中基于神经网络的过程耦合和参数升级

摘要 地球化学过程的多尺度建模需要尺度和物理之间的有效耦合。机器学习技术和神经网络的使用有可能以可接受的计算成本系统地提高模型的准确性。在本文中，我们讨论了一种在跨空间尺度的多物理模型之间传输信息的有效框架。在第一个示例中，我们根据微观地球化学反应输运模拟的结果训练浅层神经网络，并将其集成到达西尺度反应输运代码中。在第二个示例中，我们根据专用地球化学求解器生成的地球化学物种形成数据训练神经网络，并适应芯片实验室微流体实验的需要，以加速地球化学计算。反应输运模拟基准测试表明，神经网络方法在计算效率和内存要求方面都比完整物种反应输运模拟或基于查找表的方法表现更好。基于这些结果，我们讨论了每种仿真方法的优缺点以及建模算法进一步开发的潜力。