Automated Model Discovery for Soft Matter Systems

Informazioni relative al progetto

DISCOVER

ID dell’accordo di sovvenzione: 101141626

DOI

10.3030/101141626

Data della firma CE 17 Giugno 2024

Data di avvio 1 Luglio 2024

Data di completamento 30 Giugno 2029

Finanziato da

• European Research Council (ERC)

Costo totale

€ 2 775 408,00

Contributo UE

€ 2 775 408,00

Coordinato da

FRIEDRICH-ALEXANDER-UNIVERSITAET ERLANGEN-NUERNBERG
Germany

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Data Management Plan (1)

Data Management Plan

SUMMARYThroughout this project, we expect to generate both analyzed data and metadata.1. MAKING DATA FINDABLEAll generated data will be stored on Ellen Kuhl’s group repository, https://github.com/LivingMatterLab. As the PI, Ellen Kuhl will oversee all data management issues.• Experimental Data. Experimental data will be generated in the form of stretch-stress pairs during uniaxial testing and stretch-stress quadruples during biaxial testing and will be used to train, test, and validate our constitutive neural networks during model discovery. For each experiment, we will also acquire supplemented images and videos.• Analyzed Data. Analyzed data will be created during training, testing, and validation. Analyzed data will exist in the form of tables, graphs, and raw data.• Metadata. Metadata will be created both during data analysis and computational simulation. Metadata will exist in the form of data analysis programs (e.g., Matlab, Python, Jupyter notebooks) and computational simulation codes (e.g., new Abaqus user material subroutines).• Dissemination Data. Data to disseminate our results will exist in the form of course modules (slides, lecture notes, textbook on Automated Model Discovery) and open software (fully documented Automated Model Discovery platform with library of benchmark examples).2. MAKING DATA OPENLY ACCESSIBLEData will be published in peer reviewed journal articles and conference papers. Data and algorithms will be released immediately after publication. All data and algorithms will be made publicly available on the GitHub repository, https://github.com/LivingMatterLab. Other data will be released upon request. Data will be available in the following formats:• Analyzed Data. Analyzed data will be available in the form of jpg or pdf images (e.g., illustrations, graphs, bar graphs, deformation plots, movies), and binary files or excel data sheets, see https://github.com/LivingMatterLab/CANN for examples. • Necessary Metadata. Metadata will be available in the form of LaTeX, world, or pdf documents, input files for the analysis, binary files, simple programs to prototype algorithms, and probabilistic programming (e.g., Python or Jupyter notebooks). • Dissemination Data. Dissemination data will be available in the form of Powerpoint slides (e.g., lecture and seminar slides) and pdf files (e.g., lecture notes, student reports, and student data). All teaching modules resulting from this project will be made publicly available on GitHub, https://github.com/LivingMatterLab • Open Source Resources. Our Automated Model Discovery platform, the research deliverable of this project, will be publicly disseminated as an open source resource on our on the GitHub repository, https://github.com/LivingMatterLab, along with its documentation. Modules of the software, benchmark examples, and individual analysis tools, will be shared publicly in separate subfolders sorted by soft matter type.3. MAKING DATA INTEROPERABLEnot applicable; all data will be sharable4. INCREASE DATA RE-USEAll analyzed data and metadata generated during this project will be made available immediately and will remain available, both on a public server and upon request, throughout the entire duration of this project and beyond the end date.5. ALLOCATION OF RESOURCES and DATA SECURITYAll data will be shared publicly on GitHub, https://github.com/LivingMatterLab; we do not anticipate any additional cost associated with Data Security.

Pubblicazioni

Peer reviewed articles (16)

Biaxial testing and sensory texture evaluation of plant-based and animal deli meat

Autori: Skyler R. St. Pierre; Lauren Somersille Sibley; Steven Tran; Vy Tran; Ethan C. Darwin; Ellen Kuhl
Pubblicato in: Current Research in Food Science, 2025, ISSN 2665-9271
Editore: Elsevier
DOI: 10.1101/2025.02.19.639170

Discovering dispersion: How robust is automated model discovery for human myocardial tissue?

Autori: Martonová, Denisa; Leyendecker, Sigrid; Holzapfel, Gerhard A.; Kuhl, Ellen
Pubblicato in: Biomechanics and Modeling in Mechanobiology, 2025, ISSN 1617-7940
Editore: Springer
DOI: 10.1007/S10237-025-02005-X

Two for tau: Automated model discovery reveals two-stage tau aggregation dynamics in Alzheimer’s disease

Autori: Stockman, CA; Goriely, A; Kuhl, E; Jerusalem, A; Initiative, Alzheimer’s Disease Neuroimaging
Pubblicato in: Brain Multiphysics, 2024, ISSN 2666-5220
Editore: Elsevier
DOI: 10.1101/2024.07.15.603581

Material Fingerprinting: A shortcut to material model discovery without solving optimization problems

Autori: Moritz Flaschel; Denisa Martonová; Carina Veil; Ellen Kuhl
Pubblicato in: Computer Methods in Applied Mechanics and Engineering, 2026, ISSN 1879-2138
Editore: Elsevier
DOI: 10.48550/ARXIV.2508.07831

Food Research International

Autori: Reese A. Dunne; Ethan C. Darwin; Valerie A. Perez Medina; Marc E. Levenston; Skyler R. St. Pierre; Ellen Kuhl
Pubblicato in: Food Research International, 2024, ISSN 0963-9969
Editore: Elsevier
DOI: 10.1016/J.FOODRES.2025.115876

Discovering uncertainty: Gaussian constitutive neural networks with correlated weights

Autori: Jeremy A. McCulloch; Ellen Kuhl
Pubblicato in: Computational Mechanics, 2025, ISSN 1432-0924
Editore: Springer
DOI: 10.48550/ARXIV.2503.12679

Extreme Mechanics Letters

Autori: Linka, Kevin; Kuhl, Ellen
Pubblicato in: Extreme Mechanics Letters, 2024, ISSN 2352-4316
Editore: Elsevier
DOI: 10.48550/ARXIV.2404.06725

Mimicking mechanics: A comparison of meat and meat analogs

Autori: Skyler R. St. Pierre; Ellen Kuhl
Pubblicato in: Foods, 2024, ISSN 2304-8158
Editore: MDPI
DOI: 10.3390/FOODS13213495

The mechanical and sensory signature of plant-based and animal meat

Autori: Skyler R. St. Pierre; Ethan C. Darwin; Divya Adil; Magaly C. Aviles; Archer Date; Reese A. Dunne; Yanav Lall; María Parra Vallecillo; Valerie A. Perez Medina; Kevin Linka; Marc E. Levenston; Ellen Kuhl
Pubblicato in: npj Science of Food, 2024, ISSN 2396-8370
Editore: Springer Nature
DOI: 10.1038/S41538-024-00330-6

Engineering with Computers

Autori: Mathias Peirlinck; Juan A. Hurtado; Manuel K. Rausch; Adrian Buganza Tepole; Ellen Kuhl
Pubblicato in: Engineering with Computers, 2024, ISSN 0177-0667
Editore: Springer
DOI: 10.48550/ARXIV.2404.13144

Automated model discovery for tensional homeostasis: Constitutive machine learning in growth and remodeling

Autori: Hagen Holthusen; Tim Brepols; Kevin Linka; Ellen Kuhl
Pubblicato in: Computers in Biology and Medicine, 2025, ISSN 1879-0534
Editore: Elsevier
DOI: 10.18154/RWTH-2024-09844

Constitutive neural networks for main pulmonary arteries: Discovering the undiscovered

Autori: Thibault Vervenne; Mathias Peirlinck; Nele Famaey; Ellen Kuhl
Pubblicato in: Biomechanics and Modeling in Mechanobiology, 2024, ISSN 1617-7940
Editore: Springer
DOI: 10.1007/S10237-025-01930-1

Convex Neural Networks Learn Generalized Standard Material Models

Autori: Moritz Flaschel; Paul Steinmann; Laura De Lorenzis; Ellen Kuhl
Pubblicato in: Journal of the Mechanics and Physics of Solids, 2024, ISSN 1873-4782
Editore: Elsevier
DOI: 10.1016/J.JMPS.2025.106103

Generalized invariants meet constitutive neural networks: A novel framework for hyperelastic materials

Autori: Denisa Martonová; Alain Goriely; Ellen Kuhl
Pubblicato in: Journal of the Mechanics and Physics of Solids, 2026, ISSN 1873-4782
Editore: Elsevier
DOI: 10.48550/ARXIV.2508.12063

Democratizing biomedical simulation through automated model discovery and a universal material subroutine

Autori: Mathias Peirlinck; Kevin Linka; Juan A. Hurtado; Gerhard A. Holzapfel; Ellen Kuhl
Pubblicato in: Computational Mechanics, 2023, ISSN 1432-0924
Editore: Spriger
DOI: 10.1101/2023.12.06.570487

AI for food: accelerating and democratizing discovery and innovation

Autori: Ellen Kuhl
Pubblicato in: npj Science of Food, 2025, ISSN 2396-8370
Editore: Springer Nature
DOI: 10.1038/S41538-025-00441-8