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An efficient Monte Carlo strategy for elasto-plastic structures based on recurrent neural networks

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Abstract

The Monte Carlo method is computationally expensive. Especially for reliable response statistics in the tail region, a large number of samples has to be computed. In this paper, we propose a strategy that speeds up the crude Monte Carlo method using artificial intelligence. We introduce a recurrent neural network that replaces the iterative calculation procedure to evaluate nonlinear, hysteretic behavior of a structure subjected to a randomized load history. The new strategy is demonstrated on an academic example, i.e., an elasto-plastic frame, which is loaded by a time-variant force. The trained recurrent neural network reliably approximates the structures mechanical response and considerably reduces the computation time per sample.

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  1. Institute of General Mechanics (IAM), RWTH Aachen University, Templergraben 64, 52064, Aachen, Germany

    Arnd Koeppe, Franz Bamer & Bernd Markert

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Koeppe, A., Bamer, F. & Markert, B. An efficient Monte Carlo strategy for elasto-plastic structures based on recurrent neural networks. Acta Mech 230, 3279–3293 (2019). https://doi.org/10.1007/s00707-019-02436-5

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