Publications

Machine Learning

Correction: Pawelkiewicz et al. Introducing Machine Learning in Teaching Quantum Mechanics. Atoms 2025, 13, 66

Missing Supplementary Materials In the original publication [1], the Supplementary Materials was not included. The supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atoms13070066/s1. Supplementary Materials citation…

M. Pawelkiewicz, Filippo Gatti, Didier Clouteau, Viatcheslav Kokoouline, Mehdi Adrien Ayouz

Atoms - 2026

Modeling and Simulation

AI surrogate models for lifetime prediction in power electronic modules

Reliability assessment of power electronic modules (PEMs) traditionally relies on counting methods to decompose complex mission profiles (MPs) into equivalent loading cycles, followed by a cumulative damage rule to estimate fatigue life. While…

M. Ghrabli, Emanuel Aldea, Mounira Bouarroudj, Ludovic Chamoin

Microelectronics Reliability - 2026

Distributed, Parallel, and Cluster Computing

High-performance machine learning and data analysis for next-generation railway design

The MINERVE project aims to create a digital twin of the French railway infrastructure,which will then be used to assess the state of the railway and predict its evolution. To achieve such goal, the project requires a robust architecture capable of…

Hugo Gabrielidis

Health

Impact of artifact reduction using generative adversarial networks on diagnostic accuracy in cone-beam computed tomography

Objective To evaluate the impact of artifact reduction using a generative adversarial network (GAN) on the diagnostic performance for vertical root fracture (VRF) detection in cone-beam computed tomography (CBCT) images. Methods A human mandible…

Amanda Candemil Kanashiro, Hugo Gabrielidis, Filippo Gatti, Manoel Damião Sousa-Neto

Journal of Dentistry - 2026

Engineering Sciences

From data to reality: Criteria for the validation of data-driven models for history-dependent materials

This paper addresses the issue of validating identified data-driven material models for history-dependent materials, which are nowadays typically represented using neural networks. For this purpose, we introduce an a posteriori general acceptability…

Pierre Ladevèze, Ludovic Chamoin

European Journal of Mechanics - A/Solids - 2026

Engineering Sciences

Comparison of FE-based DVC and Integrated DVC for LEFM parameter calibration

In the present work, the mechanical response of a 3D printed CT specimen made of Polycarbonate was studied when subjected to an opening loading. Kinematic fields were first measured via Finite Element based Digital Volume Correlation (FE-based DVC)…

Malo Valmalle, Benjamin Smaniotto, François Hild

Theoretical and Applied Fracture Mechanics - 2026

Engineering Sciences

Benchmarking Neural Architectures for Predicting Properties in Hard-Magnetic Soft Metamaterials

Active metamaterials are materials whose mechanical properties can be tuned by external stimuli, and they have attracted growing research interest in recent years. Yet predicting their behavior remains difficult. The interaction between magnetic and…

Hai-Anh Nguyen, Cuong Ha-Minh

Lecture Notes in Civil Engineering - 2026

Mechanical engineering

Evaluation of stress intensity factors using meshless integrated digital image / volume correlation

A meshless approach is proposed for Integrated Digital Image Correlation (I-DIC) and Integrated Digital Volume Correlation (I-DVC) to measure stress intensity factors (SIFs), which is validated through synthetic experiments. Combined with double…

Yutong Hao, Lingtao Mao, Haizhou Liu, Yang Ju, François Hild

Engineering Fracture Mechanics - 2026

Materials

Feasible piezomagnetic coefficients of Fe65Co35 alloys with Cr addition

The effect of Cr addition to Fe65Co35 alloy on magnetostriction, mechanical properties and magnetomechanical properties were accessed. Based on the high saturation induction and permeability of FeCo alloys, this study analyzed the potential of Fe 65…

Iris Braga Silva, Fernando Froes, Olivier Hubert, Cristina Bormio-Nunes

Smart Materials and Structures - 2026

Engineering Sciences

Multiscale Digital Twin of the Physical Behavior of a Bio-Sourced Artificial Fiber Using Machine-Learned Interatomic Potentials: A First Attempt

Over the last decade, the rising demand for high-performance construction mate-rials has increasingly intersected with growing environmental concerns. This convergence highlights an urgent need for full decarbonization and the develop-ment of…

Cuong Ha-Minh

Lecture Notes in Civil Engineering - 2026