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Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title Empower Finite Element Software to Perform Machine Learning for Modelling Material Behaviours
Author(s) Jingzhe Pan, Mingxuan Xia, Ran He, Peter Polak, Baber Saleem
On-Site Speaker (Planned) Jingzhe Pan
Abstract Scope We present a back propagation algorithm that transforms finite element simulations of material behaviours from modelling environments into intelligent simulation platforms that can adapt, optimise and improve by learning from real world data. This algorithm is independent of the internal structure of any particular solver and requires no access to proprietary derivatives, making it suitable for integration with existing commercial FEM packages. It provides a general, solver-agnostic mechanism for learning within simulations, allowing engineers to nest ML directly inside the tools they already use and trust. The universality is demonstrated using a set of case studies covering a wide range of material behaviours, including (1) coupled electrical, thermal and mechanical analysis for deformation of materials at elevated temperatures, (2) fatigue crack propagation, (3) water absorption by engineering adhesives, and (4) degradation of biodegradable polymers. Some of the case studies have been published while others are new to this presentation.
Proceedings Inclusion? Undecided

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