Data Driven Computational Mechanics at EXascale

Informazioni relative al progetto

DComEX

ID dell’accordo di sovvenzione: 956201

DOI

10.3030/956201

Progetto chiuso

Data della firma CE 3 Dicembre 2020

Data di avvio 1 Aprile 2021

Data di completamento 30 Settembre 2024

Finanziato da

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

Costo totale

€ 2 981 250,00

Contributo UE

€ 1 359 375,00

Coordinato da

ETHNICON METSOVION POLYTECHNION
Greece

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

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

Pubblicazioni

Peer reviewed articles (22)

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

Autori: Stefanos Nikolopoulos, Ioannis Kalogeris, George Stavroulakis, Vissarion Papadopoulos
Pubblicato in: International Journal for Numerical Methods in Engineering, Numero 125, 2023, ISSN 0029-5981
Editore: 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

Autori: Petr Karnakov, Sergey Litvinov, Petros Koumoutsakos
Pubblicato in: PNAS Nexus, Numero 3, 2024, ISSN 2752-6542
Editore: 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

Autori: L. Papadopoulos, S. Bakalakos, S. Nikolopoulos, I. Kalogeris, V. Papadopoulos
Pubblicato in: International Journal of Heat and Mass Transfer, Numero 2023, 2023, ISSN 0017-9310
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.ijheatmasstransfer.2022.123420

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

Autori: 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
Pubblicato in: biorxiv, 2024, ISSN 1559-6095
Editore: Cold Spring Harbor Laboratory Press
DOI: 10.1101/2024.08.15.608042

Personalized in silico model for radiation-induced pulmonary fibrosis

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

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

Autori: JP. von Bassewitz, S. Kaltenbach, P. Koumoutsakos
Pubblicato in: arxiv, 2025, ISSN 2331-8422
Editore: Cornell University
DOI: 10.48550/arxiv.2402.00972

Interpretable learning of effective dynamics for multiscale systems

Autori: E. Menier, S. Kaltenback, M. Yagoubi, M. Schoenauer, P. Koumoutsakos
Pubblicato in: arXiv, 2025, ISSN 2331-8422
Editore: Cornell University
DOI: 10.48550/arxiv.2309.05812

Machine learning accelerated transient analysis of stochastic nonlinear structures

Autori: S. Nikolopoulos, I. Kalogeris, V. Papadopoulos
Pubblicato in: Engineering Structures, Numero Volume 257, 2022, 2022, ISSN 0141-0296
Editore: 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

Autori: 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
Pubblicato in: Neuro-Oncology Advances, Numero 6, 2024, ISSN 2632-2498
Editore: Oxford University Press
DOI: 10.1093/noajnl/vdad171

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

Autori: Stefanos Pyrialakos, Ioannis Kalogeris, Vissarion Papadopoulos
Pubblicato in: International Journal of Solids and Structures, Numero 271-272, 2024, Pagina/e 112261, ISSN 0020-7683
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.ijsolstr.2023.112261

Domain decomposition methods for 3D crack propagation problems using XFEM

Autori: S. Bakalakos, M. Georgioudakis, M. Papadrakakis
Pubblicato in: Computer Methods in Applied Mechanics and Engineering, Numero 00457825, 2022, ISSN 0045-7825
Editore: Elsevier BV
DOI: 10.1016/j.cma.2022.115390

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

Autori: Leonidas Papadopoulos, Konstantinos Atzarakis, Gerasimos Sotiropoulos, Ioannis Kalogeris, Vissarion Papadopoulos
Pubblicato in: Computer Methods in Applied Mechanics and Engineering, Numero 428, 2024, Pagina/e 117074, ISSN 0045-7825
Editore: Elsevier BV
DOI: 10.1016/j.cma.2024.117074

An efficient hierarchical Bayesian framework for multiscale material modeling

Autori: Stefanos Pyrialakos, Ioannis Kalogeris, Vissarion Papadopoulos
Pubblicato in: Composite Structures, Numero 351, 2024, Pagina/e 118570, ISSN 0263-8223
Editore: 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

Autori: H. Gao, S. Kaltenbach, P. Koumoutsakos
Pubblicato in: arXiv, 2025, ISSN 2331-8422
Editore: Cornell University
DOI: 10.48550/arxiv.2408.00157

Predictive surrogates for Aerodynamic Performance of Wind Propulsion System Configurations

Autori: M. Reche-Vilanova, S. Kaltenbach, P. Koumoutsakos, H.B. Bingham, M. Fluck, D. Morris, H.N. Psaraftis
Pubblicato in: Applied Ocean Research, 2025, ISSN 0141-1187
Editore: Pergamon Press Ltd.

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

Autori: 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
Pubblicato in: Medical Image Analysis, Numero 83, 2024, Pagina/e 102672, ISSN 1361-8415
Editore: Elsevier BV
DOI: 10.1016/j.media.2022.102672

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

Autori: H. Gao, S. Kaltenbach, P. Koumoutsakos
Pubblicato in: Nature Communications, 2025, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.48550/arxiv.2402.17157

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

Autori: M. Balcerak, J. Weidner, P. Karnakov, I. Ezhov, S. Litvinov, P. Koumoutsakos, R.Z. Zhang, J.S. Lowengrub, I. Yakushev, B. Wiestler, B. Menze
Pubblicato in: arXiv, 2025, ISSN 2331-8422
Editore: Cornell University

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

Autori: Lucas Amoudruz, Athena Economides, Georgios Arampatzis, Petros Koumoutsakos
Pubblicato in: Biophysical Journal, Numero 122, 2024, Pagina/e 1517-1525, ISSN 0006-3495
Editore: Biophysical Society
DOI: 10.1016/j.bpj.2023.03.019

Interpretable reduced-order modeling with time-scale separation

Autori: S. Kaltenbach, P.S. Koutsourelakis, P. Koumoutsakos
Pubblicato in: arXiv, 2025, ISSN 2331-8422
Editore: Cornell University
DOI: 10.48550/arxiv.2303.02189

A learnable prior improves inverse tumor growth modeling

Autori: 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
Pubblicato in: arxiv, 2025, ISSN 2331-8422
Editore: Cornell University
DOI: 10.48550/arxiv.2403.04500

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

Autori: Konstantinos Tzirakis, Christos Panagiotis Papanikas, Vangelis Sakkalis, Eleftheria Tzamali, Yannis Papaharilaou, Alfonso Caiazzo, Triantafyllos Stylianopoulos, Vasileios Vavourakis
Pubblicato in: International Journal for Numerical Methods in Biomedical Engineering, Numero 39, 2023, ISSN 2040-7939
Editore: 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

Autori: JP. von Bassewitz, S. Kaltenbach, P. Koumoutsakos
Pubblicato in: 2024
Editore: ICML-AI4Science

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

Autori: 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
Pubblicato in: Proceedings of Machine Learning Research, Numero 193, 2022, Pagina/e 566-577
Editore: Machine Learning for Health