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In aerospace applications, multiple safety regulations were introduced toaddress associated with pyrolysis. Predictive modeling of pyrolysis is achallenging task since multiple thermo-chemo-mechanical laws need to beconcurrently solved at each time step. So far, classical modeling approacheswere mostly focused on defining the basic chemical processes (pyrolysis andignite) at micro-scale by decoupling them from thermal solution at themicro-scale and then validating them using meso-scale experimental results. Theadvent of Machine Learning (ML) and AI in recent years has provided anopportunity to construct quick surrogate ML models to replace high fidelitymulti-physics models, which have a high computational cost and may not beapplicable for high nonlinear equations. This serves as the motivation for theintroduction of innovative Physics informed neural networks (PINNs) to simulatemultiple stiff, and semi-stiff ODEs that govern Pyrolysis and Ablation. OurEngine is particularly developed to calculate the char formation and degree ofburning in the course of pyrolysis of crosslinked polymeric systems. Amulti-task learning approach is hired to assure the best fitting to thetraining data. The proposed Hybrid-PINN (HPINN) solver was bench-marked againstfinite element high fidelity solutions on different examples. We developed PINNarchitectures using collocation training to forecast temperature distributionsand the degree of burning in the course of pyrolysis in multiple one- andtwo-dimensional examples. By decoupling thermal and mechanical equations, wecan predict the loss of performance in the system by predicting the charformation pattern and localized degree of burning at each continuum.

A Knowledge-driven Physics-Informed Neural Network model; Pyrolysis and Ablation of Polymers