A comparison of two neural network architectures for fast structural response prediction

PAMM, vol. 21

Abstract

Abstract In this contribution, we compare two different neural network architectures to predict the response statistics of structures. The overall goal is a significant speed‐up of the numerically expensive Monte Carlo simulation. The first approach is based on a convolutional neural network that learns from the whole excitation history, whereas the second approach is based on a feed‐forward network architecture learning from hand‐designed features. Both procedures use supervised learning: The neural networks learn from an initial subset before the prediction of the response statistics of the Monte Carlo simulation is possible.

Authors 3

1. Denny Thaler corresponding Aachen Institute of General Mechanics

   RWTH Aachen University

   Affiliation as printed

   Institute of General Mechanics RWTH Aachen University Eilfschornsteinstr. 18 52062 Aachen

   Denny Thaler

   Email: [email protected]

   Institute of General Mechanics, RWTH Aachen University, Eilfschornsteinstr. 18, 52062 Aachen

   Telephone: +49 241 80 94601

2. Franz Bamer Aachen Institute of General Mechanics

   RWTH Aachen University

   Affiliation as printed

   Institute of General Mechanics RWTH Aachen University Eilfschornsteinstr. 18 52062 Aachen

   Institute of General Mechanics, RWTH Aachen University, Eilfschornsteinstr. 18, 52062 Aachen

3. Bernd A. Markert Aachen Institute of General Mechanics

   RWTH Aachen University

   Affiliation as printed

   Institute of General Mechanics RWTH Aachen University Eilfschornsteinstr. 18 52062 Aachen

   Institute of General Mechanics, RWTH Aachen University, Eilfschornsteinstr. 18, 52062 Aachen