Real-time inversion using self-explainable deep learning driven by expert knowledge

Informations projet

IN-DEEP

N° de convention de subvention: 101119556

DOI

10.3030/101119556

Date de signature de la CE 13 Juin 2023

Date de début 1 Février 2024

Date de fin 31 Janvier 2028

Financé au titre de

• Marie Skłodowska-Curie Actions (MSCA)

Coût total

Aucune donnée

Contribution de l’UE

€ 2 046 614,40

Coordonné par

UNIVERSIDAD DEL PAIS VASCO/ EUSKAL HERRIKO UNIBERTSITATEA
Spain

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 (9)

Theoretical advances for Deep PDE solvers.

Initial report about the main theoretical results and comparison between the different Deep PDE solvers.

Baseline model for PDE enhancement with collected data.

Characterization of the techniques developed to enrich PDEs utilizing available data.

Datasets requirements for parametric PDEs.

Generation, analysis, and sampling of the dataset in parametric PDEs.

Gender and diversity equality plan.

Priorities, concrete objectives, and the specific measures that will be implemented to improve gender and diversity equality within the IN-DEEP project.

Training events in M1-24

Description and self-assessment of training events and proposed correction measures.

Computational complexity control through adaptive strategies in Deep PDE solvers for parametric problems.

Efficiency assessment of the novel adaptive strategies developed for Deep PDE solvers applied to parametric problems.

Optimization techniques for Deep PDE solvers.

Analysis of the effect of adaptive strategies on the algorithms.

Advances in theory and numerical approximation of Deep PDE solvers for parametric problems.

Initial Analytical study and partial numerical validation of the proposed methodology. Comparison with conventional solvers.

Loss function choice criteria, according to the PDE problem.

Study of the appropriateness and efficiency of the loss function in Deep PDE solvers. Advantages, limitations, recommendations for forward, inverse, and parametric problems.

Websites, patent fillings, videos etc. (1)

Website

Project website, logo and social media page creation

Publications

Book chapters (2)

EXPBrain: Exponential Integrators for Glioblastoma Brain Tumor Simulations

Auteurs: Magdalena Pabisz, Dominika Ciupek, Askold Vilkha, Maciej Paszyński
Publié dans: Lecture Notes in Computer Science, Computational Science – ICCS 2025 Workshops, 2025, ISBN 978-3-031-97554-7
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-97554-7_10

Tensorial Implementation for Robust Variational Physics-Informed Neural Networks

Auteurs: Askold Vilkha, Carlos Uriarte, Paweł Maczuga, Tomasz Służalec, Maciej Paszyński
Publié dans: Lecture Notes in Computer Science, Computational Science – ICCS 2025, 2025, ISBN 978-3-031-97626-1
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-97626-1_5

Peer reviewed articles (2)

Collocation-based robust variational physics-informed neural networks (CRVPINNs)

Auteurs: Marcin Łoś, Tomasz Służalec, Paweł Maczuga, Askold Vilkha, Carlos Uriarte, Maciej Paszyński
Publié dans: Computers & Structures, Numéro 316, 2025, ISSN 0045-7949
Éditeur: Elsevier BV
DOI: 10.1016/J.COMPSTRUC.2025.107839

Computer Methods in Applied Mechanics and Engineering

Auteurs: Shima Baharlouei; Jamie M. Taylor; Carlos Uriarte; David Pardo
Publié dans: Computer Methods in Applied Mechanics and Engineering, 2024, ISSN 0045-7825
Éditeur: Elsevier BV
DOI: 10.2139/SSRN.5004047