Physics-Informed Neural Networks and Sequence Encoder: Application to heating and early cooling of thermo-stamping process

TL;DR

This paper extends the PINN-SE framework to complex, real-world thermo-stamping heating and cooling processes, demonstrating its ability to handle multimodal data and improve generalization from synthetic to real data.

Contribution

It introduces the application of PINN-SE to a realistic thermo-stamping scenario and explores multimodal data integration and synthetic data training for better real-world generalization.

Findings

PINN-SE can be applied to complex thermo-stamping processes.

Training on synthetic data improves real data generalization.

Combining multiple encoders enhances model performance.

Abstract

In a previous work (Elaarabi et al., 2025b), the Sequence Encoder for online dynamical system identification (Elaarabi et al., 2025a) and its combination with PINN (PINN-SE) were introduced and tested on both synthetic and real data case scenarios. The sequence encoder is able to effectively encode time series into feature vectors, which the PINN then uses to map to dynamical behavior, predicting system response under changes in parameters, ICs and BCs. Previously (Elaarabi et al., 2025b), the tests on real data were limited to simple 1D problems and only 1D time series inputs of the Sequence Encoder. In this work, the possibility of applying PINN-SE to a more realistic case is investigated: heating and early cooling of the thermo-stamping process, which is a critical stage in the forming process of continuous fiber reinforced composite materials with thermoplastic polymer. The possibility of extending the PINN-SE inputs to multimodal data, such as sequences of temporal 2D images and to scenarios involving variable geometries, is also explored. The results show that combining multiple encoders with the previously proposed method (Elaarabi et al., 2025b) is feasible, we also show that training the model on synthetic data generated based on experimental data can help the model to generalize well for real experimental data, unseen during the training phase.