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AUTOmating MATerial modeling for composable and learnable behaviors

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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, high-performance open-source library, the project will enable flexible modeling of complex material behaviors like plasticity, viscoelasticity, and damage mechanics. To improve generalizability and data efficiency compared to black-box ML models, the AUTOMATIX framework incorporates mathemat

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recordType

award

status

SIGNED

region

EU

value

1928198

unit

EUR