We present a Physics-Informed Neural Network (PINN) to simulate the
thermochemical evolution of a composite material on a tool undergoing cure in
an autoclave. In particular, we solve the governing coupled system of
differential equations -- including conductive heat transfer and resin cure
kinetics -- by optimizing the parameters of a deep neural network (DNN) using a
physics-based loss function. To account for the vastly different behaviour of
thermal conduction and resin cure, we design a PINN consisting of two
disconnected subnetworks, and develop a sequential training algorithm that
mitigates instability present in traditional training methods. Further, we
incorporate explicit discontinuities into the DNN at the composite-tool
interface and enforce known physical behaviour directly in the loss function to
improve the solution near the interface. We train the PINN with a technique
that automatically adapts the weights on the loss terms corresponding to PDE,
boundary, interface, and initial conditions. Finally, we demonstrate that one
can include problem parameters as an input to the model -- resulting in a
surrogate that provides real-time simulation for a range of problem settings --
and that one can use transfer learning to significantly reduce the training
time for problem settings similar to that of an initial trained model. The
performance of the proposed PINN is demonstrated in multiple scenarios with
different material thicknesses and thermal boundary conditions.

本研究提出一种基于物理知识约束的神经网络（PINN），用于模拟复合材料在固化炉中的热化学演变，并且该网络能有效地求解包括传热和树脂固化动力学等方程组。在 PINN 中，为了解决热传导和树脂固化行为的差异，设计了由两个不连通子网络组成。此外，还采用了一种对 PDE、边界、界面和初始条件自适应调整损失项权重的技术。最后，我们证明了 PINN 可用于实时模拟材料不同厚度和热边界条件下的多种场景，并且可以使用迁移学习来显著减少解决类似问题的训练时间。