Reduced-Order Models for Ranking Damage Initiation in Dual-Phase Composites Using Bayesian Neural Networks

JOM ◽  
2020 ◽  
Vol 72 (12) ◽  
pp. 4359-4369
Author(s):  
Aditya Venkatraman ◽  
David Montes de Oca Zapiain ◽  
Surya R. Kalidindi
Keyword(s):  
Neural Networks ◽  
Dual Phase ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Bayesian Neural Networks ◽  
Damage Initiation

Design of microwave devices by segmentation, finite elements, reduced-order models, and neural networks

2007 ◽  
Vol 49 (3) ◽  
pp. 655-659
Author(s):  
Juan M. Cid ◽  
Jesús García ◽  
Javier Monge ◽  
Juan Zapata
Keyword(s):  
Neural Networks ◽  
Finite Elements ◽  
Microwave Devices ◽  
Reduced Order Models ◽  
Reduced Order

scholarly journals POD-Enhanced Deep Learning-Based Reduced Order Models for the Real-Time Simulation of Cardiac Electrophysiology in the Left Atrium

2021 ◽  
Vol 12 ◽  
Author(s):  
Stefania Fresca ◽  
Andrea Manzoni ◽  
Luca Dedè ◽  
Alfio Quarteroni
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Left Atrium ◽  
Real Time ◽  
Cardiac Electrophysiology ◽  
Front Propagation ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Training Costs ◽  
Speed Up

The numerical simulation of multiple scenarios easily becomes computationally prohibitive for cardiac electrophysiology (EP) problems if relying on usual high-fidelity, full order models (FOMs). Likewise, the use of traditional reduced order models (ROMs) for parametrized PDEs to speed up the solution of the aforementioned problems can be problematic. This is primarily due to the strong variability characterizing the solution set and to the nonlinear nature of the input-output maps that we intend to reconstruct numerically. To enhance ROM efficiency, we proposed a new generation of non-intrusive, nonlinear ROMs, based on deep learning (DL) algorithms, such as convolutional, feedforward, and autoencoder neural networks. In the proposed DL-ROM, both the nonlinear solution manifold and the nonlinear reduced dynamics used to model the system evolution on that manifold can be learnt in a non-intrusive way thanks to DL algorithms trained on a set of FOM snapshots. DL-ROMs were shown to be able to accurately capture complex front propagation processes, both in physiological and pathological cardiac EP, very rapidly once neural networks were trained, however, at the expense of huge training costs. In this study, we show that performing a prior dimensionality reduction on FOM snapshots through randomized proper orthogonal decomposition (POD) enables to speed up training times and to decrease networks complexity. Accuracy and efficiency of this strategy, which we refer to as POD-DL-ROM, are assessed in the context of cardiac EP on an idealized left atrium (LA) geometry and considering snapshots arising from a NURBS (non-uniform rational B-splines)-based isogeometric analysis (IGA) discretization. Once the ROMs have been trained, POD-DL-ROMs can efficiently solve both physiological and pathological cardiac EP problems, for any new scenario, in real-time, even in extremely challenging contexts such as those featuring circuit re-entries, that are among the factors triggering cardiac arrhythmias.

Reduced-order models of structures with viscoelastic components

AIAA Journal ◽  
1999 ◽  
Vol 37 ◽  
pp. 1318-1325 ◽  
Author(s):  
Michael I. Friswell ◽  
Daniel J. Inman
Keyword(s):  
Reduced Order Models ◽  
Reduced Order

Investigation of Sampling Strategies for Reduced-Order Models of Rocket Combustors

2021 ◽  
Author(s):  
Christopher R. Wentland ◽  
Cheng Huang ◽  
Karthikeyan Duraisamy
Keyword(s):  
Reduced Order Models ◽  
Sampling Strategies ◽  
Reduced Order
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