Deep learning-based reduced order models for the real-time simulation of the nonlinear dynamics of microstructures

by   Stefania Fresca, et al.

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 nonlinearities. In the first phase, a limited number of high fidelity snapshots are used to generate a POD-Galerkin ROM which is subsequently exploited to generate the data, covering the whole parameter range, used in the training phase of the DL-ROM. A convolutional autoencoder is employed to map the system response onto a low-dimensional representation and, in parallel, to model the reduced nonlinear trial manifold. The system dynamics on the manifold is described by means of a deep feedforward neural network that is trained together with the autoencoder. The strategy is benchmarked against high fidelity solutions on a clamped-clamped beam and on a real micromirror with softening response and multiplicity of solutions. By comparing the different computational costs, we discuss the impressive gain in performance and show that the DL-ROM truly represents a real-time tool which can be profitably and efficiently employed in complex system-level simulation procedures for design and optimisation purposes.


page 15

page 18

page 21

page 23


Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models

Simulating fluid flows in different virtual scenarios is of key importan...

A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs

Traditional reduced order modeling techniques such as the reduced basis ...

Learning Low-Dimensional Quadratic-Embeddings of High-Fidelity Nonlinear Dynamics using Deep Learning

Learning dynamical models from data plays a vital role in engineering de...

Deep learning-based reduced order models in cardiac electrophysiology

Predicting the electrical behavior of the heart, from the cellular scale...

Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches

Micro-Electro-Mechanical-Systems are complex structures, often involving...

Learning two-phase microstructure evolution using neural operators and autoencoder architectures

Phase-field modeling is an effective mesoscale method for capturing the ...

Lensless multicore-fiber microendoscope for real-time tailored light field generation with phase encoder neural network (CoreNet)

The generation of tailored light with multi-core fiber (MCF) lensless mi...

Please sign up or login with your details

Forgot password? Click here to reset