High-dimensional spatio-temporal dynamics can often be encoded in a low-dimensional subspace. Engineering applications for modeling, characterization, design, and control of such large-scale systems often rely on dimensionality reduction to make solutions computationally tractable in real-time. Common existing paradigms for dimensionality reduction include linear methods, such as the singular value decomposition (SVD), and nonlinear methods, such as variants of convolutional autoencoders (CAE). However, these encoding techniques lack the ability to efficiently represent the complexity associated with spatio-temporal data, which often requires variable geometry, non-uniform grid resolution, adaptive meshing, and/or parametric dependencies. To resolve these practical engineering challenges, we propose a general framework called Neural Implicit Flow (NIF) that enables a mesh-agnostic, low-rank representation of large-scale, parametric, spatial-temporal data. NIF consists of two modified multilayer perceptrons (MLPs): (i) ShapeNet, which isolates and represents the spatial complexity, and (ii) ParameterNet, which accounts for any other input complexity, including parametric dependencies, time, and sensor measurements. We demonstrate the utility of NIF for parametric surrogate modeling, enabling the interpretable representation and compression of complex spatio-temporal dynamics, efficient many-spatial-query tasks, and improved generalization performance for sparse reconstruction.
翻译:用于模拟、定性、设计和控制这类大型系统的工程应用往往依赖于维度减少,以使解决方案在实时中可进行计算。为了解决这些实际的工程挑战,我们提议了一个称为神经隐性流动(NIF)的总框架,使大规模、负数模型、空间时空数据具有中位、低级代表性。 NIF 由两种经过修改的多级透视(MLPs)组成:(i) ShapeNet,它隔离并代表了各种空间复杂度、可变性能、可变性能、可变性能、可变性能、可变性能、可变性能、可变性能、可变性能、可变性能、可变性能、可变性能、可变性能、可变性能、可变性能、可变性能等。