A comparison of two neural network architectures for fast structural response prediction. Issue 1 (14th December 2021) - Thaler, Denny; Bamer, Franz; Markert, Bernd
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.