Thermal conductivity is a crucial material property for a diverse range of energy
technologies, ranging from thermal management of high power electronics to thermal …

In this paper, we propose a general framework for projection-based model order reduction
assisted by deep neural networks. The proposed methodology, called ROM-net, consists in …

Nonlinear model order reduction has opened the door to parameter optimization and
uncertainty quantification in complex physics problems governed by nonlinear equations. In …

We propose a projection‐based model order reduction procedure for a general class of
parametric quasi‐static problems in nonlinear mechanics with internal variables. The …

We consider the dictionary-based ROM-net (Reduced Order Model) framework [Daniel et al.,
Adv. Model. Simul. Eng. Sci. 7 (2020) https://doi. org/10.1186/s40323-020-00153-6] and …

A new computational technique for the reduction of multi-time scale systems is proposed in
this paper. The reduction process is performed based on the dominant poles preservation in …

In this chapter, we introduce the solution space for high-fidelity models based on partial
differential equations and the finite element model. The manifold learning approach to …

In the field of nuclear power plant management, Electricité de France (EDF) strives to ensure
acomprehensive understanding of the mechanical state of the nuclear containment …

Consider first data-based machine learning techniques. They rely on large sets of examples
provided during the training stage and do not learn with equations. Dealing with a situation …

Uncertainty quantification in computational physics requires running many simulations. For
some industrial applications, the complexity of the numerical model is incompatible with the …