Project description
Enhanced material modelling for complex behaviours
Constitutive models play an important role in the prediction of material behaviour. However, the conventional approach is limited, especially for complex material behaviour. The ERC-funded AUTOMATIX project focuses on the improvement of material modelling using the concept of machine learning along with the principles of solid mechanics. It will introduce material-informed neural networks (MINNs), which will incorporate empirical data along with mathematical concepts. The project will develop a modular open-source library for the modelling of complex material behaviour such as plasticity and damage mechanics. With a focus on 3D printed fibre-reinforced concrete, the project will address the issues of anisotropy and curing. The overall goal is to ensure the availability of reliable tools for the accurate prediction of material behaviour.
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)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences physical sciences thermodynamics
- natural sciences computer and information sciences computational science multiphysics
- natural sciences mathematics applied mathematics mathematical model
You need to log in or register to use this function
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
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.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
-
HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
See all projects funded under this programme
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.
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.
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
See all projects funded under this funding scheme
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2025-COG
See all projects funded under this callHost institution
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.
77455 Marne La Vallee Cedex 2
France
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.