T. Hoang

A computational stochastic multiscale methodology for MEMS structures involving adhesive contact

J. Golinval, L. Noels, M. Arnst, 2017 .

Stochastic Multiscale Model of MEMS Stiction Accounting for High-Order Statistical Moments of Non-Gaussian Contacting Surfaces

J. Golinval, L. Noels, M. Arnst, 2018, Journal of Microelectromechanical Systems.

Characterization, propagation, and sensitivity analysis of uncertainties in the directed energy deposition process using a deep learning-based surrogate model

A. Habraken, L. Duchene, H. Tran, 2022, Probabilistic Engineering Mechanics.

Molecular dynamics simulation of nanoindentation in Al and Fe: On the influence of system characteristics

N. Gunkelmann, T. Hoang, H. Luu, 2021, Applied Surface Science.

A study of dry stiction phenomenon in MEMS using a computational stochastic multi-scale methodology

J. Golinval, L. Noels, M. Arnst, 2016, 2016 17th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE).

Capabilities of Auto-encoders and Principal Component Analysis of the reduction of microstructural images; Application on the acceleration of Phase-Field simulations

A. Habraken, L. Duchene, H. Tran, 2023, Computational Materials Science.

Data-driven Prediction of Temperature Evolution in Metallic Additive Manufacturing Process

A. Habraken, L. Duchene, H. Tran, 2021 .

A probabilistic model for predicting the uncertainties of the humid stiction phenomenon on hard materials

Ludovic Noels, Ling Wu, R. Müller, 2015, J. Comput. Appl. Math..

Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learning

A. Habraken, L. Duchene, H. Tran, 2022, Journal of Intelligent Manufacturing.