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About this Abstract
Meeting 2027 TMS Annual Meeting & Exhibition
Symposium Algorithms Development in Materials Science and Engineering
Presentation Title A No-Code Graphical Framework for Forward and Inverse Physics-Informed Neural Networks
Author(s) Asfandyar Khan, Mahmood Mamivand
On-Site Speaker (Planned) Asfandyar Khan
Abstract Scope Physics-informed neural networks are increasingly used for solving and identifying PDE-governed systems, but setting up custom PINN problems often requires substantial programming experience. This work presents a graphical software framework that enables users to configure forward and inverse PINN studies without writing code. Built as a user-facing interface around DeepXDE, the platform allows users to define domains, variables, equations, initial and boundary conditions, observed data, trainable parameters, network settings, and training schedules through an interactive workflow. Additional modules support visualization, loss monitoring, parameter tracking, and comparison between predictions and reference data. The framework is being tested on one- and two-dimensional problems, including heat diffusion, Allen–Cahn, and Cahn–Hilliard spinodal decomposition. By lowering the coding barrier while preserving flexibility for custom PDEs, the software aims to make PINN-based modeling and inverse analysis more accessible across materials science and engineering applications.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation

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