通过替代地球化学建模加速水泥系统中的反应输运模拟：深度神经网络和k-近邻
Speeding up reactive transport simulations in cement systems by surrogate geochemical modeling: deep neural networks and k-nearest neighbors
摘要：
我们通过将RT代码中的地球化学求解器替换为代理模型或仿真器来加速反应性输运（RT）模拟，考虑经过训练的深度神经网络（DNN）或k-最近邻（kNN）回归量。我们专注于2D浸出硬化水泥浆体扩散或平流-分散运输条件下，硅酸钙水合物和4或7个化学成分的固溶体表示，并使用HPx（耦合Hydrus PHREEQC模型）反应运输代码作为基线。我们发现，经过训练，我们的基于DNN和基于kNN的代码HPx-DNN和HPx-kNN都可以令人满意地做出非常准确的预测，同时与HPx相比，提供了3到9的加速因子，具有4个核心的并行地球化学计算。以单线程HPx为基准，这些加速因子变为8到33。总的来说，HPx-DNN是HPx-kNN被发现在GPU上执行DNN回归和kNN搜索时实现接近最佳的加速比。重要的是，对于更复杂的7组分水泥体系，无法设计出在可能的地球化学条件的整个空间上全局准确的仿真器。相反，我们因此构建“本地”仿真器，这些仿真器仅在输入参数空间的相关部分上有效。这个空间是通过运行一个粗略的，因此计算便宜的全RT模拟，并随后通过内核密度采样探索。未来的工作将集中在提高这种类型的水泥系统的准确性。
英文摘要：
We accelerate reactive transport (RT) simulation by replacing the geochemical solver in the RT code by a surrogate model or emulator, considering either a trained deep neural network (DNN) or a k-nearest neighbor (kNN) regressor. We focus on 2D leaching of hardened cement paste under diffusive or advective-dispersive transport conditions, a solid solution representation of the calcium silicate hydrates and either 4 or 7 chemical components, and use the HPx (coupled Hydrus-PHREEQC model) reactive transport code as baseline. We find that after training, both our DNN-based and kNN-based codes, HPx-DNN and HPx-kNN, can make satisfactorily to very accurate predictions while providing either a 3 to 9 speedup factor compared to HPx with parallelized geochemical calculations over 4 cores. Benchmarking against single-threaded HPx, these speedup factors become 8 to 33. Overall, HPx-DNN are HPx-kNN are found to achieve a close to optimal speedup when DNN regression and kNN search are performed on a GPU. Importantly, for the more complex 7-components cement system, no emulator that is globally accurate over the full space of possible geochemical conditions could be devised. Instead we therefore build "local" emulators that are only valid over a relevant fraction of the input parameter space. This space is identified by running a coarse and thus computationally cheap full RT simulation, and subsequently explored by kernel density sampling. Future work will focus on improving accuracy for this type of cement systems.
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。
