A5087811055- Superconducting Materials and Applications
- Fusion materials and technologies
- Magnetic confinement fusion research
- Probabilistic and Robust Engineering Design
- Nuclear reactor physics and engineering
- Particle accelerators and beam dynamics
- Radiation Detection and Scintillator Technologies
- Advanced Multi-Objective Optimization Algorithms
- Nuclear Physics and Applications
- Rheology and Fluid Dynamics Studies
- Nuclear and radioactivity studies
- Neural Networks and Applications
- Nuclear Materials and Properties
- Elasticity and Material Modeling
- Model Reduction and Neural Networks
- Optimal Experimental Design Methods
- Fatigue and fracture mechanics
- Polymer Nanocomposites and Properties
- Seismic Performance and Analysis
- Structural Health Monitoring Techniques
- Medical Imaging Techniques and Applications
- Advanced X-ray and CT Imaging
- Innovations in Concrete and Construction Materials
- NMR spectroscopy and applications
- Structural Engineering and Vibration Analysis
Commissariat à l'Énergie Atomique et aux Énergies Alternatives
2010-2024
CEA Paris-Saclay
2006-2024
Université Paris-Saclay
2017-2024
Maison de la Simulation
2023-2024
Direction des énergies
1999-2022
ITER
2012-2022
Laboratoire de Mécanique et d’Acoustique
2003-2021
Move United
2015
Centre National de la Recherche Scientifique
2011
CEA Paris-Saclay - Etablissement de Saclay
2005-2008
Summary This article deals with sensitivity of the response pounding buildings respect to structural and earthquake excitation parameters. A comprehensive analysis is carried out by means Monte Carlo simulations adjacent single degree freedom impacting oscillators. analysis, based on Sobol's method, computes indexes which provide a consistent measure relative importance parameters such as dimensionless main frequency, mass frequency ratios structures, coefficient restitution. Moreover,...
This paper addresses the use of experimental data for calibrating a computer model and improving its predictions underlying physical system. A global statistical approach is proposed in which bias between system modeled as realization Gaussian process. The application classical inference to this yields rigorous method adding correction based on data. can substantially improve calibrated predicting new conditions. Furthermore, quantification uncertainty prediction provided. Physical expertise...
The high-performance computing resources and the constant improvement of both numerical simulation accuracy experimental measurements with which they are confronted bring a new compulsory step to strengthen credence given results: uncertainty quantification. This can have different meanings, according requested goals (rank sources, reduce them, estimate precisely critical threshold or an optimal working point), it could request mathematical methods greater lesser complexity. paper introduces...
It is now common practice in nuclear engineering to base extensive studies on numerical computer models.These require run codes potentially thousands of configurations and without expert individual controls the computational physical aspects each simulations.In this paper, we compare different statistical metamodeling techniques show how metamodels can help improve global behaviour these studies.We consider Germinal thermalmechanical code by Kriging, kernel regression neural networks.Kriging...
This article presents a physics-informed deep learning method for the quantitative estimation of spatial coordinates gamma interactions within monolithic scintillator, with focus on Positron Emission Tomography (PET) imaging. A Density Neural Network approach is designed to estimate 2-dimensional photon interaction in fast lead tungstate (PbWO4) scintillator detector. We introduce custom loss function inherent uncertainties associated reconstruction process and incorporate physical...