Deepaneurysm / Deepaneurysm

**DeepAneurysm // DeepAneurysm**:

- Réf **ABG-131267**
**ADUM-65320**
- Sujet de Thèse
- 18/04/2025
- Université de Technologie de Compiègne
- Lieu de travail- Compiègne cedex - France
- Intitulé du sujet- DeepAneurysm // DeepAneurysm
- Champs scientifiques- Mathématiques
- Mots clés- anévrisme aortique abdominal, rupture, fluid-structure interaction, modélisation numérique, machine learning, physics-aware model
aortic addominal aneurysm, rupture, fluid-structure interaction, numerical model, machine learning, physics-aware model

**Description du sujet**:

- The idea behind our project is to modernize and leverage numerical Abdominal Aortic Aneurysm (AAA) simulations with Artificial Intelligence (AI) to make their use easier in the medical community and the procurement of the biomechanical patient-specific data possible within less than one minute, which is a real challenge. On the fundamental science side, and as mentioned above, our objective is to investigate state-of-the-art data-driven identification algorithms for high-dimensional spatio-temporal problems from previous high-fidelity simulations, possibly readjusted with patient data/measurements. We hope they will enable live visualizations of quantities and fields of interest (stress fields, risk assessment according to the evolution) for decision-making (surgical decision, surgical procedure).

Current computational Physics-based solvers can provide accurate results but are too time-consuming for medical use. A breakthrough is thus needed to lower the computational time by at least 3 or 4 orders of magnitude. We believe that a smart synergy between Full-Order models (FOM) and AI-based strategies can take up this challenge.

The idea behind our project is to modernize and leverage numerical Abdominal Aortic Aneurysm (AAA) simulations with Artificial Intelligence (AI) to make their use easier in the medical community and the procurement of the biomechanical patient-specific data possible within less than one minute, which is a real challenge. On the fundamental science side, and as mentioned above, our objective is to investigate state-of-the-art data-driven identification algorithms for high-dimensional spatio-temporal problems from previous high-fidelity simulations, possibly readjusted with patient data/measurements. We hope they will enable live visualizations of quantities and fields of interest (stress fields, risk assessment according to the evolution) for decision-making (surgical decision, surgical procedure).

Current computational Physics-based solvers can provide accurate results but are too time-consuming for medical use. A breakthrough is thus needed to lower the computational time by at least 3 or 4 orders of magnitude. We believe that a smart synergy between Full-Order models (FOM) and AI-based strategies can take up this challenge.

Début de la thèse : 01/10/2025

**Nature du financement**:
**Précisions sur le financement**:

- Financement d'un établissement public Français

**Présentation établissement et labo d'accueil**:

- Université de Technologie de Compiègne

**Etablissement délivrant le doctorat**:

- Université de Technologie de Compiègne

**Ecole doctorale**:

- 71 Sciences pour l'ingénieur
- Etudiant MSc ou équivalent Analyse numérique Calcul scientifique Mécanique des fluides, mécanique des structures Machine learning / Scientifique Machine Learning Reduced-order models, deep learning Software, python programming
MSc student Scientific Computing Fluid mechanics, solid mechanics Machine learning / Scientifique Machine Learning Reduced-order models, deep learning Software, python programming- 05/05/2025