We consider the problem of using SciML to predict solutions of high Mach fluid flows over irregular geometries. In this setting, data is limited, and so it is desirable for models to perform well in the low-data setting. We show that Neural Basis Functions (NBF), which learns a basis of behavior modes from the data and then uses this basis to make predictions, is more effective than a basis-unaware baseline model. In addition, we identify continuing challenges in the space of predicting solutions for this type of problem.

翻译：我们考虑利用科学机器学习（SciML）预测不规则几何体上高马赫流体流动解的问题。在该场景中，数据有限，因此模型在低数据条件下仍能保持良好性能至关重要。我们证明，神经基函数（NBF）——通过数据学习行为模式基函数，并利用该基函数进行预测——相较于不感知基函数的基线模型更为有效。此外，我们指出了在此类问题预测求解空间中持续存在的挑战。