中子神经元：一种用于域分解中子输运问题计算负载估计的Transformer模型
Neurons for Neutrons: A Transformer Model for Computation Load Estimation on Domain-Decomposed Neutron Transport Problems
AI总结：
本文提出一种带三维输入嵌入的Transformer模型，用于预测域分解中子输运问题的子域计算负载，在SMR模拟上达到98.2%准确率并可跳过小规模模拟步骤。
摘要：
域分解是一种用于降低大型中子输运问题内存开销的技术。目前，这些域的最优负载均衡处理器分配通常通过问题的小规模模拟来确定，这对研究人员而言耗时较长，且每当问题输入发生变化时都必须重复进行。我们提出了一种Transformer模型，该模型具有独特的三维输入嵌入，以及专为域分解中子输运问题设计的输入表示，能够预测由小规模模拟生成的子域计算负载。我们证明，在域分解的小型模块化反应堆（SMR）模拟上训练的这种模型达到了98.2%的准确率，同时能够完全跳过小规模模拟步骤。本文还讨论了该模型在变体燃料组件、其他问题几何形状以及模拟参数变化下的鲁棒性测试。
英文摘要：
Domain decomposition is a technique used to reduce memory overhead on large neutron transport problems. Currently, the optimal load-balanced processor allocation for these domains is typically determined through small-scale simulations of the problem, which can be time-consuming for researchers and must be repeated anytime a problem input is changed. We propose a Transformer model with a unique 3D input embedding, and input representations designed for domain-decomposed neutron transport problems, which can predict the subdomain computation loads generated by small-scale simulations. We demonstrate that such a model trained on domain-decomposed Small Modular Reactor (SMR) simulations achieves 98.2% accuracy while being able to skip the small-scale simulation step entirely. Tests of the model's robustness on variant fuel assemblies, other problem geometries, and changes in simulation parameters are also discussed.
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。
