论文标题
没有物理定律建模的物理系统
Physical Systems Modeled Without Physical Laws
论文作者
论文摘要
基于物理学的模拟通常与复杂的可区分方程以及许多科学和几何输入相结合。我们的工作涉及从这些模拟中收集数据，并了解基于树的机器学习方法如何模拟所需的输出而不“知道”模拟中涉及的复杂支持。选定的基于物理学的模拟包括Navier-Stokes，应力分析和电磁场线，以基准性能作为数值和统计算法。我们特别着重于在两个模拟输出和增加空间分辨率之间预测特定的时空数据，以将物理预测推广到更精细的测试网格，而无需重复数值计算的计算成本。
Physics-based simulations typically operate with a combination of complex differentiable equations and many scientific and geometric inputs. Our work involves gathering data from those simulations and seeing how well tree-based machine learning methods can emulate desired outputs without "knowing" the complex backing involved in the simulations. The selected physics-based simulations included Navier-Stokes, stress analysis, and electromagnetic field lines to benchmark performance as numerical and statistical algorithms. We specifically focus on predicting specific spatial-temporal data between two simulation outputs and increasing spatial resolution to generalize the physics predictions to finer test grids without the computational costs of repeating the numerical calculation.
