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 …

Abstract Model reduction is a method that maps full-order conservation equations into lower-
order subspaces or establish a data-driven surrogate model to reduce the complexity of the …

When learning simulations for modeling physical phenomena in industrial designs,
geometrical variabilities are of prime interest. While classical regression techniques prove …

The homogenization-based streamline stiffener path optimization (SSPO) method was
previously proposed by the authors for stiffener layout design of non-uniform curved grid …

This contribution discusses surrogate models that emulate the solution field (s) in the entire
simulation domain. The surrogate uses the most characteristic modes of the solution field (s) …

Physical simulations are at the core of many critical industrial systems. However, today's
physical simulators have some limitations such as computation time, dealing with missing or …

For materials under cyclic thermal loadings, temperature and strain rate-dependent creep
deformation can occur due to the thermal expansion mismatch near material interfaces …

The two-layer quasi-geostrophic equations (2QGE) are a simplified model that describes the
dynamics of a stratified, wind-driven ocean in terms of potential vorticity and stream function …

This paper devises an efficient reduced-order model based on a unified viscoplastic
constitutive model for predicting creep–fatigue behavior under multiaxial loading. In 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 …