AutoPINN：当AutoML遇到物理信息神经网络时
AutoPINN: When AutoML Meets Physics-Informed Neural Networks
摘要：
物理信息神经网络（PINN）最近被提出来解决科学和工程问题，其中物理定律被引入到神经网络中作为先验知识。通过嵌入的物理定律，PINN可以通过可观察的变量来估计通过物理工具无法观察到的关键参数。例如，电力电子转换器（PEC）是绿色能源转型的重要组成部分。PINN已经被应用于使用电流和电压来估计电容，这在PEC操作期间是不可观察的，这在操作期间可以容易地观察到。估计的电容有助于PEC的自诊断。现有的PINN通常是手动设计的，这很耗时，并且由于神经网络架构和超参数的大量设计选择，可能会导致性能不佳。此外，PINN通常部署在不同的物理设备上，例如，PEC，资源有限且不同。因此，它需要在不同的资源约束下设计不同的PINN模型，这使得手工设计更具挑战性。为了应对这些挑战，我们提出了自动物理信息神经网络（AutoPINN），这是一个通过结合AutoML和PINN来实现PINN自动设计的框架。具体来说，我们首先定制一个搜索空间，允许找到高精度PINN的PEC内部参数估计。然后，我们提出了一个资源感知的搜索策略，探索搜索空间，以找到最好的PINN模型在不同的资源约束。我们通过实验证明，AutoPINN能够使用更少的资源找到比人类设计的最先进的PINN模型更准确的PINN模型。
英文摘要：
Physics-Informed Neural Networks (PINNs) have recently been proposed to solve scientific and engineering problems, where physical laws are introduced into neural networks as prior knowledge. With the embedded physical laws, PINNs enable the estimation of critical parameters, which are unobservable via physical tools, through observable variables. For example, Power Electronic Converters (PECs) are essential building blocks for the green energy transition. PINNs have been applied to estimate the capacitance, which is unobservable during PEC operations, using current and voltage, which can be observed easily during operations. The estimated capacitance facilitates self-diagnostics of PECs. Existing PINNs are often manually designed, which is time-consuming and may lead to suboptimal performance due to a large number of design choices for neural network architectures and hyperparameters. In addition, PINNs are often deployed on different physical devices, e.g., PECs, with limited and varying resources. Therefore, it requires designing different PINN models under different resource constraints, making it an even more challenging task for manual design. To contend with the challenges, we propose Automated Physics-Informed Neural Networks (AutoPINN), a framework that enables the automated design of PINNs by combining AutoML and PINNs. Specifically, we first tailor a search space that allows finding high-accuracy PINNs for PEC internal parameter estimation. We then propose a resource-aware search strategy to explore the search space to find the best PINN model under different resource constraints. We experimentally demonstrate that AutoPINN is able to find more accurate PINN models than human-designed, state-of-the-art PINN models using fewer resources.
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。
