Neural-Parareal：动态训练神经算子作为粗求解器用于聚变磁流体动力学模拟的时间并行化
Neural-Parareal: Dynamically Training Neural Operators as Coarse Solvers for Time-Parallelisation of Fusion MHD Simulations
AI总结：
本文提出Neural-Parareal框架，在Parareal时间并行化过程中动态利用生成数据训练神经算子作为粗求解器，以逐步提高聚变磁流体动力学模拟的加速效果，展示了HPC与AI融合在数字工程设计中的价值。
摘要：
聚变研究设施ITER目前正在组装中，以证明聚变可用于工业能源生产，同时世界范围内的其他几个项目也在推进，如EU-DEMO、CFETR、SPARC和STEP。托卡马克的高工程复杂性使其成为极难优化的装置，基于测试的优化将过于缓慢且成本过高。相反，必须优先采用数字化设计与优化，这需要强耦合的高性能计算（HPC）计算套件。在此背景下，拥有能够提供快速估计并带有不确定性量化的代理模型对于探索和优化新的设计方案至关重要。此外，这些代理模型反过来又可用于从一开始就加速模拟。Parareal就是这种情况，它是一种时间并行化方法，可以加速大规模HPC模拟，其中粗求解器可以被代理模型替代。本文开发了一个新颖的框架Neural-Parareal，用于随着更多数据的获得而动态地整合神经算子的训练。对于给定的输入参数域，随着使用Parareal运行更多模拟，该算法产生的大量数据被用于训练新的代理模型，以作为未来Parareal模拟的粗求解器，从而逐步获得更准确的粗求解器，并因此实现更高的加速比。研究发现，这种神经网络代理模型在利用Parareal提供加速方面可以比传统粗求解器有效得多。这项研究展示了HPC与AI的融合，这种融合必须成为数字工程设计领域的普遍实践。
英文摘要：
The fusion research facility ITER is currently being assembled to demonstrate that fusion can be used for industrial energy production, while several other programmes across the world are also moving forward, such as EU-DEMO, CFETR, SPARC and STEP. The high engineering complexity of a tokamak makes it an extremely challenging device to optimise, and test-based optimisation would be too slow and too costly. Instead, digital design and optimisation must be favored, which requires strongly-coupled suites of High-Performance Computing calculations. In this context, having surrogate models to provide quick estimates with uncertainty quantification is essential to explore and optimise new design options. Furthermore, these surrogates can in turn be used to accelerate simulations in the first place. This is the case of Parareal, a time-parallelisation method that can speed-up large HPC simulations, where the coarse-solver can be replaced by a surrogate. A novel framework, Neural-Parareal, is developed to integrate the training of neural operators dynamically as more data becomes available. For a given input-parameter domain, as more simulations are being run with Parareal, the large amount of data generated by the algorithm is used to train new surrogate models to be used as coarse-solvers for future Parareal simulations, leading to progressively more accurate coarse-solvers, and thus higher speed-up. It is found that such neural network surrogates can be much more effective than traditional coarse-solver in providing a speed-up with Parareal. This study is a demonstration of the convergence of HPC and AI which simply has to become common practice in the world of digital engineering design.
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。
