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Meeting 2024 TMS Annual Meeting & Exhibition
Symposium Frontiers of Materials Award Symposium: Physics-Informed Machine Learning for Modeling and Design of Materials and Manufacturing Processes
Sponsorship TMS Materials Processing and Manufacturing Division
TMS: Integrated Computational Materials Engineering Committee
Organizer(s) Pinar Acar, Virginia Tech
Scope This Frontiers of Materials event will provide a forum on “physics-informed machine learning (ML)” with applications to the (multi-scale) modeling and design of material systems and manufacturing processes. While data-driven ML methods have found a wide range of applications in materials modeling, design, and manufacturing, the predictions of purely data-driven models may still be physically inconsistent due to observational biases. Recent research in materials informatics has addressed this challenge by enforcing underlying physical constitutive rules, constraints, and initial/boundary conditions in data-driven model development, thereby creating physics-informed ML models. These “domain-aware” physics-informed ML models are shown to achieve better prediction accuracy and interpretability even when trained with small datasets, in addition to faster training and improved generalization performance compared to purely data-driven approaches.

This event will welcome presentations on modeling, multi-scale modeling, and the design of materials and manufacturing processes across different length scales (ranging from the atomistic scale to the macro-scale) using physics-informed ML techniques. Studies on the integration of physics-informed ML models into experimental datasets of materials and manufacturing processes will also be of interest.

Additional topics are listed as follows:

- Application of physics-informed ML to small datasets of materials and manufacturing processes
- Application of physics-informed ML to big datasets of materials and manufacturing processes
- Transfer learning approaches combined with physics-informed ML to study physical material behavior across different length scales
- Uncertainty quantification for material systems and manufacturing processes with the aid of physics-informed ML
- Application of physics-informed ML in metal additive manufacturing
- Application of physics-informed ML in constitutive model development for materials behavior

Abstracts Due 07/15/2023
Proceedings Plan Planned: None Selected

A Machine-learning Based Hierarchical Framework to Discover Novel Functional Materials
Adaptive Surrogate Models using Unbalanced Data for Material Design
Interpretability and Generalizability of Constitutive Models using Symbolic Regression
Inverse Design for Crystal Plasticity Model Identification via Physics-Informed Neural Networks
Physics-Aware Recurrent Convolutional Neural Networks for Modeling Hotspot Formation and Growth in Energetic Materials
Physics-Informed Machine Learning for Scan Path Optimization

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