Data Driven Computational Mechanics at EXascale

Informations projet

DComEX

N° de convention de subvention: 956201

DOI

10.3030/956201

Projet clôturé

Date de signature de la CE 3 Decembre 2020

Date de début 1 Avril 2021

Date de fin 30 Septembre 2024

Financé au titre de

• INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)

Coût total

€ 2 981 250,00

Contribution de l’UE

€ 1 359 375,00

Coordonné par

ETHNICON METSOVION POLYTECHNION
Greece

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Documents, reports (12)

AI-Solve library prototype (report)
Communication, dissemination and exploitation report (report)
DMAPand CAE surrogate model Report

DMAP and CAE surrogate model Report

Communication, dissemination and exploitation report and plan (report)
Performance evaluation of DCoMEX-BIO prototype (report)
Communication, dissemination and exploitation plan (report)
System requirements, design and architecture (report)
Performance evaluation of DCoMEX-MAT prototype (report)
Data processing software final evaluation and documentation (report)Data processing software final evaluation and documentation (report)

Data processing software final evaluation and documentation (report)

Collaboration plan with definition of common objectives and activities including Milestones (report)

Collaboration plan with definition of common objectives and activities including Milestones. Updated versions will be delivered at the end of each reporting period (report)

AI-Solve performance evaluation (report)
DCoMEX codes baseline prototype (report)

Other (6)

UQ aware image segmentation final software (software module)
Optimal material composition for CNT reinforced concrete applications
Post-processing final software (software module)
DCoMEX codes baseline prototype (software module)
UQ aware image segmentation prototype (software module)
DMAP algorithm prototype

Open Research Data Pilot (1)

Data management plan (report)

Websites, patent fillings, videos etc. (1)

DCoMEX website

Publications

Peer reviewed articles (22)

AI‐enhanced iterative solvers for accelerating the solution of large‐scale parametrized systems

Auteurs: Stefanos Nikolopoulos, Ioannis Kalogeris, George Stavroulakis, Vissarion Papadopoulos
Publié dans: International Journal for Numerical Methods in Engineering, Numéro 125, 2023, ISSN 0029-5981
Éditeur: John Wiley & Sons Inc.
DOI: 10.1002/nme.7372

Solving inverse problems in physics by optimizing a discrete loss: Fast and accurate learning without neural networks

Auteurs: Petr Karnakov, Sergey Litvinov, Petros Koumoutsakos
Publié dans: PNAS Nexus, Numéro 3, 2024, ISSN 2752-6542
Éditeur: Oxford University Press
DOI: 10.1093/pnasnexus/pgae005

A computational framework for the indirect estimation of the interface thermal resistance of composite materials using XPINNs

Auteurs: L. Papadopoulos, S. Bakalakos, S. Nikolopoulos, I. Kalogeris, V. Papadopoulos
Publié dans: International Journal of Heat and Mass Transfer, Numéro 2023, 2023, ISSN 0017-9310
Éditeur: Pergamon Press Ltd.
DOI: 10.1016/j.ijheatmasstransfer.2022.123420

Quantitative 3D histochemistry reveals region-specific amyloid-β reduction by the antidiabetic drug netoglitazone

Auteurs: Francesca Catto, Ehsan Dadgar-Kiani, Daniel Kirschenbaum, Athena E. Economides, Anna Maria Reuss, Chiara Trevisan, Davide Caredio, Delic Mirzet, Lukas Frick, Ulrike Weber-Stadlbauer, Sergey Litvinov, Petros Koumoutsakos, Jin Hyung Lee, Adriano Aguzzi
Publié dans: biorxiv, 2024, ISSN 1559-6095
Éditeur: Cold Spring Harbor Laboratory Press
DOI: 10.1101/2024.08.15.608042

Personalized in silico model for radiation-induced pulmonary fibrosis

Auteurs: E. Ioannou, M. Hadjicharalambous, A. Malai, E. Papageorgiou, A. Peraticou, N. Katodritis, D. Vomvas, V. Vavourakis
Publié dans: Journal of the Royal Society Interface, 2024, ISSN 1742-5662
Éditeur: Zenodo
DOI: 10.5281/zenodo.13766990

Closure Discovery for Coarse-grained Partial Differential Equations with Grid-based Reinforcement Learning

Auteurs: JP. von Bassewitz, S. Kaltenbach, P. Koumoutsakos
Publié dans: arxiv, 2025, ISSN 2331-8422
Éditeur: Cornell University
DOI: 10.48550/arxiv.2402.00972

Interpretable learning of effective dynamics for multiscale systems

Auteurs: E. Menier, S. Kaltenback, M. Yagoubi, M. Schoenauer, P. Koumoutsakos
Publié dans: arXiv, 2025, ISSN 2331-8422
Éditeur: Cornell University
DOI: 10.48550/arxiv.2309.05812

Machine learning accelerated transient analysis of stochastic nonlinear structures

Auteurs: S. Nikolopoulos, I. Kalogeris, V. Papadopoulos
Publié dans: Engineering Structures, Numéro Volume 257, 2022, 2022, ISSN 0141-0296
Éditeur: Pergamon Press Ltd.
DOI: 10.1016/j.engstruct.2022.114020

Toward image-based personalization of glioblastoma therapy: A clinical and biological validation study of a novel, deep learning-driven tumor growth model

Auteurs: Marie-Christin Metz, Ivan Ezhov, Jan C Peeken, Josef A Buchner, Jana Lipkova, Florian Kofler, Diana Waldmannstetter, Claire Delbridge, Christian Diehl, Denise Bernhardt, Friederike Schmidt-Graf, Jens Gempt, Stephanie E Combs, Claus Zimmer, Bjoern Menze, Benedikt Wiestler
Publié dans: Neuro-Oncology Advances, Numéro 6, 2024, ISSN 2632-2498
Éditeur: Oxford University Press
DOI: 10.1093/noajnl/vdad171

Multiscale analysis of nonlinear systems using a hierarchy of deep neural networks

Auteurs: Stefanos Pyrialakos, Ioannis Kalogeris, Vissarion Papadopoulos
Publié dans: International Journal of Solids and Structures, Numéro 271-272, 2024, Page(s) 112261, ISSN 0020-7683
Éditeur: Pergamon Press Ltd.
DOI: 10.1016/j.ijsolstr.2023.112261

Domain decomposition methods for 3D crack propagation problems using XFEM

Auteurs: S. Bakalakos, M. Georgioudakis, M. Papadrakakis
Publié dans: Computer Methods in Applied Mechanics and Engineering, Numéro 00457825, 2022, ISSN 0045-7825
Éditeur: Elsevier BV
DOI: 10.1016/j.cma.2022.115390

Fusing nonlinear solvers with transformers for accelerating the solution of parametric transient problems

Auteurs: Leonidas Papadopoulos, Konstantinos Atzarakis, Gerasimos Sotiropoulos, Ioannis Kalogeris, Vissarion Papadopoulos
Publié dans: Computer Methods in Applied Mechanics and Engineering, Numéro 428, 2024, Page(s) 117074, ISSN 0045-7825
Éditeur: Elsevier BV
DOI: 10.1016/j.cma.2024.117074

An efficient hierarchical Bayesian framework for multiscale material modeling

Auteurs: Stefanos Pyrialakos, Ioannis Kalogeris, Vissarion Papadopoulos
Publié dans: Composite Structures, Numéro 351, 2024, Page(s) 118570, ISSN 0263-8223
Éditeur: Elsevier BV
DOI: 10.1016/j.compstruct.2024.118570

Generative Learning of the Solution of Parametric Partial Differential Equations using Guided Diffusion Models and Virtual Observations

Auteurs: H. Gao, S. Kaltenbach, P. Koumoutsakos
Publié dans: arXiv, 2025, ISSN 2331-8422
Éditeur: Cornell University
DOI: 10.48550/arxiv.2408.00157

Predictive surrogates for Aerodynamic Performance of Wind Propulsion System Configurations

Auteurs: M. Reche-Vilanova, S. Kaltenbach, P. Koumoutsakos, H.B. Bingham, M. Fluck, D. Morris, H.N. Psaraftis
Publié dans: Applied Ocean Research, 2025, ISSN 0141-1187
Éditeur: Pergamon Press Ltd.

Learn-Morph-Infer: A new way of solving the inverse problem for brain tumor modeling

Auteurs: Ivan Ezhov, Kevin Scibilia, Katharina Franitza, Felix Steinbauer, Suprosanna Shit, Lucas Zimmer, Jana Lipkova, Florian Kofler, Johannes C. Paetzold, Luca Canalini, Diana Waldmannstetter, Martin J. Menten, Marie Metz, Benedikt Wiestler, Bjoern Menze
Publié dans: Medical Image Analysis, Numéro 83, 2024, Page(s) 102672, ISSN 1361-8415
Éditeur: Elsevier BV
DOI: 10.1016/j.media.2022.102672

Generative Learning for the Effective Dynamics of Complex High-dimensional Systems

Auteurs: H. Gao, S. Kaltenbach, P. Koumoutsakos
Publié dans: Nature Communications, 2025, ISSN 2041-1723
Éditeur: Nature Publishing Group
DOI: 10.48550/arxiv.2402.17157

Individualizing Glioma Radiotherapy planning by Optimization of a Data and Physics-Informed Discrete Loss

Auteurs: M. Balcerak, J. Weidner, P. Karnakov, I. Ezhov, S. Litvinov, P. Koumoutsakos, R.Z. Zhang, J.S. Lowengrub, I. Yakushev, B. Wiestler, B. Menze
Publié dans: arXiv, 2025, ISSN 2331-8422
Éditeur: Cornell University

The stress-free state of human erythrocytes: Data-driven inference of a transferable RBC model

Auteurs: Lucas Amoudruz, Athena Economides, Georgios Arampatzis, Petros Koumoutsakos
Publié dans: Biophysical Journal, Numéro 122, 2024, Page(s) 1517-1525, ISSN 0006-3495
Éditeur: Biophysical Society
DOI: 10.1016/j.bpj.2023.03.019

Interpretable reduced-order modeling with time-scale separation

Auteurs: S. Kaltenbach, P.S. Koutsourelakis, P. Koumoutsakos
Publié dans: arXiv, 2025, ISSN 2331-8422
Éditeur: Cornell University
DOI: 10.48550/arxiv.2303.02189

A learnable prior improves inverse tumor growth modeling

Auteurs: J. Weidner, I. Ezhov, M. Metz, S. Litvinov, S. Kaltenbach, M. Balcerak, L. Feiner, L. Lux, F. Kofler, J. Lipkova, J. latz, D. Rueckert, B. Menze, B. Wiestler
Publié dans: arxiv, 2025, ISSN 2331-8422
Éditeur: Cornell University
DOI: 10.48550/arxiv.2403.04500

An adaptive semi‐implicit finite element solver for brain cancer progression modeling

Auteurs: Konstantinos Tzirakis, Christos Panagiotis Papanikas, Vangelis Sakkalis, Eleftheria Tzamali, Yannis Papaharilaou, Alfonso Caiazzo, Triantafyllos Stylianopoulos, Vasileios Vavourakis
Publié dans: International Journal for Numerical Methods in Biomedical Engineering, Numéro 39, 2023, ISSN 2040-7939
Éditeur: John Wiley & Sons Ltd.
DOI: 10.1002/cnm.3734

Conference proceedings (2)

Improving the accuracy of Coarse-grained Partial Differential Equations with Grid-based Reinforcement Learning

Auteurs: JP. von Bassewitz, S. Kaltenbach, P. Koumoutsakos
Publié dans: 2024
Éditeur: ICML-AI4Science

A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling

Auteurs: I. Ezhov, M. Rosier, L. Zimmer, F. Kofler, S. Shit, J.C. Paetzold, K. Scibilia, F. Steinbauer, L. Maechler, K. Franitza, T. Amiranashvili, M.J. Menten, M. Metz, S. Conjeti, B. Wiestler, B. Menze
Publié dans: Proceedings of Machine Learning Research, Numéro 193, 2022, Page(s) 566-577
Éditeur: Machine Learning for Health