ERC Consolidator Grant · 2025
AUTOmating MATerial modeling for composable and learnable behaviors
AUTOMATIX addresses challenges in constitutive material modeling by integrating machine learning with existing material knowledge in solid mechanics. Constitutive models are crucial for predicting material behavior under various loading and environmental conditions, yet traditional approaches often struggle to represent complex, non-linear, and time-dependent behaviors, limiting their accuracy across engineering applications. This project aims to bridge this gap by developing Material-Informed Neural Networks (MINNs), which combine empirical data with established mathematical structures to enhance interpretability, data efficiency, and predictive accuracy. By creating a modular,…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.