Training nonlinear parametrizations such as deep neural networks to
numerically approximate solutions of partial differential equations is often
based on minimizing a loss that includes the residual, which is analytically
available in limited settings only. At the same time, empirically estimating
the training loss is challenging because residuals and related quantities can
have high variance, especially for transport-dominated and high-dimensional
problems that exhibit local features such as waves and coherent structures.
Thus, estimators based on data samples from un-informed, uniform distributions
are inefficient. This work introduces Neural Galerkin schemes that estimate the
training loss with data from adaptive distributions, which are empirically
represented via ensembles of particles. The ensembles are actively adapted by
evolving the particles with dynamics coupled to the nonlinear parametrizations
of the solution fields so that the ensembles remain informative for estimating
the training loss. Numerical experiments indicate that few dynamic particles
are sufficient for obtaining accurate empirical estimates of the training loss,
even for problems with local features and with high-dimensional spatial
domains.

通过使用 Neural Galerkin schemes 方法，利用集合较少的粒子动态适应粒子来近似求解偏微分方程的深度神经网络训练过程中的误差，并在具有局部特征和高维空间域的问题中获得了准确的实证误差估计。