Automated Model Discovery for Soft Matter Systems

Projektinformationen

DISCOVER

ID Finanzhilfevereinbarung: 101141626

DOI

10.3030/101141626

EK-Unterschriftsdatum 17 Juni 2024

Startdatum 1 Juli 2024

Enddatum 30 Juni 2029

Finanziert unter

• European Research Council (ERC)

Gesamtkosten

€ 2 775 408,00

EU-Beitrag

€ 2 775 408,00

Koordiniert durch

FRIEDRICH-ALEXANDER-UNIVERSITAET ERLANGEN-NUERNBERG
Germany

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

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.

Veröffentlichungen

Peer reviewed articles (37)

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

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

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

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

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

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

Probing mycelium mechanics and taste: The moist and fibrous signature of fungi steak

Autoren: Thibault Vervenne, Skyler R. St. Pierre, Nele Famaey, Ellen Kuhl
Veröffentlicht in: Acta Biomaterialia, Ausgabe 202, 2025, ISSN 1742-7061
Herausgeber: Elsevier BV
DOI: 10.1016/J.ACTBIO.2025.07.002

Material Fingerprinting for rapid discovery of hyperelastic models: First experimental validation

Autoren: Denisa Martonová, Ellen Kuhl, Moritz Flaschel
Veröffentlicht in: Journal of the Mechanics and Physics of Solids, Ausgabe 208, 2026, ISSN 0022-5096
Herausgeber: Elsevier BV
DOI: 10.1016/J.JMPS.2025.106463

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

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

Texture profile analysis and rheology of plant-based and animal meat

Autoren: Reese A. Dunne, Ethan C. Darwin, Valerie A. Perez Medina, Marc E. Levenston, Skyler R. St. Pierre, Ellen Kuhl
Veröffentlicht in: Food Research International, Ausgabe 205, 2025, ISSN 0963-9969
Herausgeber: Elsevier BV
DOI: 10.1016/J.FOODRES.2025.115876

Open-Source Benchmarking of Plant-Based and Animal Meats

Autoren: Sybren D. van den Bedem, Ellen Kuhl, Caroline Cotto
Veröffentlicht in: Foods, Ausgabe 15, 2026, ISSN 2304-8158
Herausgeber: MDPI AG
DOI: 10.3390/FOODS15122112

Food Research International

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

A universal material model subroutine for soft matter systems

Autoren: Mathias Peirlinck, Juan A. Hurtado, Manuel K. Rausch, Adrián Buganza Tepole, Ellen Kuhl
Veröffentlicht in: Engineering with Computers, Ausgabe 41, 2025, ISSN 0177-0667
Herausgeber: Springer Science and Business Media LLC
DOI: 10.1007/S00366-024-02031-W

Artificial intelligence for food innovation

Autoren: Bianca Datta, Markus J. Buehler, Yvonne Chow, Kristina Gligorić, Dan Jurafsky, David L. Kaplan, Rodrigo Ledesma-Amaro, Giorgia Del Missier, Lisa Neidhardt, Karim Pichara, Benjamin Sanchez-Lengeling, Miek Schlangen, Skyler R. St. Pierre, Ilias Tagkopoulos, Anna Thomas, Nik Watson, Ellen Kuhl
Veröffentlicht in: Nature Food, 2026, ISSN 2662-1355
Herausgeber: Springer Science and Business Media LLC
DOI: 10.1038/S43016-026-01380-7

Discovering uncertainty: Gaussian constitutive neural networks with correlated weights

Autoren: Jeremy A. McCulloch; Ellen Kuhl
Veröffentlicht in: Computational Mechanics, 2025, ISSN 1432-0924
Herausgeber: Springer
DOI: 10.48550/ARXIV.2503.12679

Extreme Mechanics Letters

Autoren: Linka, Kevin; Kuhl, Ellen
Veröffentlicht in: Extreme Mechanics Letters, 2024, ISSN 2352-4316
Herausgeber: Elsevier
DOI: 10.48550/ARXIV.2404.06725

Mimicking mechanics: A comparison of meat and meat analogs

Autoren: Skyler R. St. Pierre; Ellen Kuhl
Veröffentlicht in: Foods, 2024, ISSN 2304-8158
Herausgeber: MDPI
DOI: 10.3390/FOODS13213495

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

Autoren: 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
Veröffentlicht in: npj Science of Food, 2024, ISSN 2396-8370
Herausgeber: Springer Nature
DOI: 10.1038/S41538-024-00330-6

A generalized dual potential for inelastic Constitutive Artificial Neural Networks: A JAX implementation at finite strains

Autoren: Hagen Holthusen, Kevin Linka, Ellen Kuhl, Tim Brepols
Veröffentlicht in: Journal of the Mechanics and Physics of Solids, Ausgabe 206, 2026, ISSN 0022-5096
Herausgeber: Elsevier BV
DOI: 10.1016/J.JMPS.2025.106337

Generative artificial intelligence creates delicious, sustainable, and nutritious burgers

Autoren: Vahidullah Tac, Christopher D. Gardner, Ellen Kuhl
Veröffentlicht in: npj Science of Food, Ausgabe 10, 2026, ISSN 2396-8370
Herausgeber: Springer Science and Business Media LLC
DOI: 10.1038/S41538-026-00953-X

Engineering with Computers

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

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

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

The mechanics and physics of tofu: Understanding hydrated soft solids through feature networks

Autoren: Birte Boes; Jaan-Willem Simon; Hagen Holthusen; Ellen Kuhl
Veröffentlicht in: Journal of the Mechanics and Physics of Solids, 2026, ISSN 1873-4782
Herausgeber: Elsevier
DOI: 10.64898/2025.12.10.693552

A convex route to thermoelasticity: Learning internal energy and dissipation

Autoren: Hagen Holthusen, Paul Steinmann, Ellen Kuhl
Veröffentlicht in: Computer Methods in Applied Mechanics and Engineering, Ausgabe 459, 2026, ISSN 0045-7825
Herausgeber: Elsevier BV
DOI: 10.1016/J.CMA.2026.119082

Computer Methods in Applied Mechanics and Engineering

Autoren: Denisa Martonová, Mathias Peirlinck, Kevin Linka, Gerhard A. Holzapfel, Sigrid Leyendecker, Ellen Kuhl
Veröffentlicht in: Computer Methods in Applied Mechanics and Engineering, 2024, ISSN 0045-7825
Herausgeber: Elsevier
DOI: 10.1016/J.CMA.2024.117078

Automated model discovery for textile structures: The unique mechanical signature of warp knitted fabrics

Autoren: Jeremy A. McCulloch , Ellen Kuhl
Veröffentlicht in: Acta Biomaterialia, 2024, ISSN 1742-7061
Herausgeber: Elsevier BV
DOI: 10.1016/J.ACTBIO.2024.09.051

A complement to neural networks for anisotropic inelasticity at finite strains

Autoren: Hagen Holthusen, Ellen Kuhl
Veröffentlicht in: Computer Methods in Applied Mechanics and Engineering, Ausgabe 450, 2026, ISSN 0045-7825
Herausgeber: Elsevier BV
DOI: 10.1016/J.CMA.2025.118612

Constitutive neural networks for main pulmonary arteries: Discovering the undiscovered

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

Convex Neural Networks Learn Generalized Standard Material Models

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

Generative AI for material design: A mechanics perspective from burgers to matter

Autoren: Vahidullah Tac, Ellen Kuhl
Veröffentlicht in: Computer Methods in Applied Mechanics and Engineering, Ausgabe 461, 2026, ISSN 0045-7825
Herausgeber: Elsevier BV
DOI: 10.1016/J.CMA.2026.119171

Non-smooth optimization meets automated material model discovery

Autoren: Moritz Flaschel, Trevor Hastie, Ellen Kuhl
Veröffentlicht in: Journal of Computational Physics, Ausgabe 563, 2026, ISSN 0021-9991
Herausgeber: Elsevier BV
DOI: 10.1016/J.JCP.2026.115043

The meatball matchup: Plant vs. animal proteins on campus

Autoren: Skyler R. St. Pierre, Aeneas O. Koosis, Nancy Zhang, Ellen Kuhl
Veröffentlicht in: Food Research International, Ausgabe 240, 2026, ISSN 0963-9969
Herausgeber: Elsevier BV
DOI: 10.1016/J.FOODRES.2026.119631

Discovering uncertainty: Bayesian constitutive artificial neural networks

Autoren: Kevin Linka, Gerhard A. Holzapfel, Ellen Kuhl
Veröffentlicht in: Computer Methods in Applied Mechanics and Engineering, Ausgabe 433, 2025, ISSN 0045-7825
Herausgeber: Elsevier BV
DOI: 10.1016/J.CMA.2024.117517

Best-in-class modeling: A novel strategy to discover constitutive models for soft matter systems

Autoren: Kevin Linka, Ellen Kuhl
Veröffentlicht in: Extreme Mechanics Letters, Ausgabe 70, 2024, ISSN 2352-4316
Herausgeber: Elsevier BV
DOI: 10.1016/J.EML.2024.102181

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

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

A note on constitutive artificial neural networks for finite strain plasticity

Autoren: Tim van der Velden, Birte Boes, Tim Brepols, Ellen Kuhl, Hagen Holthusen
Veröffentlicht in: Mechanics Research Communications, Ausgabe 155, 2026, ISSN 0093-6413
Herausgeber: Elsevier BV
DOI: 10.1016/J.MECHRESCOM.2026.104707

Texture Independently Drives Liking in AI-Generated Alternative Protein Burgers

Autoren: Vahidullah Tac, Aeneas O. Koosis, Ellen Kuhl
Veröffentlicht in: Foods, Ausgabe 15, 2026, ISSN 2304-8158
Herausgeber: MDPI AG
DOI: 10.3390/FOODS15112026

Mechanical, rheological, and sensory characterization of lion’s mane mushroom steak

Autoren: Skyler R. St. Pierre, Lucas Boyle, Thibault Vervenne, Ethan C. Darwin, Marie A. Goodson, Manuel Palomares, Nancy Zhang, Ellen Kuhl
Veröffentlicht in: Current Research in Food Science, Ausgabe 12, 2026, ISSN 2665-9271
Herausgeber: Elsevier BV
DOI: 10.1016/J.CRFS.2026.101403

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

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

AI for food: accelerating and democratizing discovery and innovation

Autoren: Ellen Kuhl
Veröffentlicht in: npj Science of Food, 2025, ISSN 2396-8370
Herausgeber: Springer Nature
DOI: 10.1038/S41538-025-00441-8