In aerospace applications, multiple safety regulations were introduced to address associated with pyrolysis. Predictive modeling of pyrolysis is a challenging task since multiple thermo-chemo-mechanical laws need to be concurrently solved at each time step. So far, classical modeling approaches were mostly focused on defining the basic chemical processes (pyrolysis and ignite) at micro-scale by decoupling them from thermal solution at the micro-scale and then validating them using meso-scale experimental results. The advent of Machine Learning (ML) and AI in recent years has provided an opportunity to construct quick surrogate ML models to replace high fidelity multi-physics models, which have a high computational cost and may not be applicable for high nonlinear equations. This serves as the motivation for the introduction of innovative Physics informed neural networks (PINNs) to simulate multiple stiff, and semi-stiff ODEs that govern Pyrolysis and Ablation. Our Engine is particularly developed to calculate the char formation and degree of burning in the course of pyrolysis of crosslinked polymeric systems. A multi-task learning approach is hired to assure the best fitting to the training data. The proposed Hybrid-PINN (HPINN) solver was bench-marked against finite element high fidelity solutions on different examples. We developed PINN architectures using collocation training to forecast temperature distributions and the degree of burning in the course of pyrolysis in multiple one- and two-dimensional examples. By decoupling thermal and mechanical equations, we can predict the loss of performance in the system by predicting the char formation pattern and localized degree of burning at each continuum.

中文翻译：

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知识驱动的物理信息神经网络模型；聚合物的热解和烧蚀

在航空航天应用中，引入了多项安全法规来解决与热解相关的问题。热解的预测建模是一项具有挑战性的任务，因为需要在每个时间步同时解决多个热化学机械定律。到目前为止，经典建模方法主要集中在定义微观尺度的基本化学过程（热解和点燃），方法是将它们与微观尺度的热溶液解耦，然后使用中尺度实验结果对其进行验证。近年来机器学习 (ML) 和 AI 的出现为构建快速替代 ML 模型提供了机会，以替代计算成本高且可能不适用于高非线性方程的高保真多物理模型。这是引入创新的基于物理的神经网络 (PINN) 来模拟控制热解和消融的多个刚性和半刚性 ODE 的动机。我们的引擎专门用于计算交联聚合物系统热解过程中的焦炭形成和燃烧程度。采用多任务学习方法来确保最适合训练数据。提出的 Hybrid-PINN (HPINN) 求解器在不同示例上针对有限元高保真解决方案进行了基准测试。我们使用搭配训练开发了 PINN 架构，以预测多个一维和二维示例中热解过程中的温度分布和燃烧程度。通过解耦热和力学方程，