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math.NA2021
Deep learning-based reduced order models for the real-time simulation of the nonlinear dynamics of microstructures
Stefania Fresca, Giorgio Gobat, Patrick Fedeli +2
We propose a non-intrusive Deep Learning-based Reduced Order Model (DL-ROM) capable of capturing the complex dynamics of mechanical systems showing inertia and geometric nonlineari…
physics.flu-dyn2021★ 2 cited
Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models
Stefania Fresca, Andrea Manzoni
Simulating fluid flows in different virtual scenarios is of key importance in engineering applications. However, high-fidelity, full-order models relying, e.g., on the finite eleme…
math.NA2021★ 14 cited
POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition
Stefania Fresca, Andrea Manzoni
Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional reduced order models (ROMs) - built, e.g., thro…