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AUTOmating MATerial modeling for composable and learnable behaviors

Objective

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 mathematical structures and partial material knowledge directly into a modern machine learning architecture. This gray-box approach allows MINNs to require less data while providing interpretable predictions aligned with known physical principles like thermodynamics. The framework is expected to enhance modeling accuracy in civil and mechanical engineering while advancing data-driven material modeling in multiphysics and multiscale systems. The project will test its approach on real-world applications of 3D printed fiber-reinforced concrete, which presents distinct challenges like layer-wise anisotropy, complex curing conditions, and various damage mechanisms, necessitating models that capture both microstructural and macroscale responses. By merging innovations from machine learning with material knowledge, the project aims to provide a reliable framework for improved material behavior prediction, offering valuable tools for researchers and engineers across multiple fields.

Fields of science (EuroSciVoc)

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Keywords

Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)

Programme(s)

Multi-annual funding programmes that define the EU’s priorities for research and innovation.

Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) ERC-2025-COG

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Host institution

ECOLE NATIONALE DES PONTS ET CHAUSSEES
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 912 415,50
Address
AVENUE BLAISE PASCAL-CITE DESCARTES-CHAMPS-SUR-MARNE 6-8
77455 Marne La Vallee Cedex 2
France

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Region
Ile-de-France Ile-de-France Seine-et-Marne
Activity type
Higher or Secondary Education Establishments
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 928 198,00

Beneficiaries (2)

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