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David Ryckelynck (Mines ParisTech): Computer vision with error estimation for reduced-order modeling

15 Feb 2019   10:30-11:30

Computer vision enables recommending a reduced order model for fast stress prediction according to various possible loading environments. This approach is applied on a macroscopic part by using a digital image of a mechanical test. We propose a hybrid approach that simultaneously exploits a data-driven model and a physics-based model, in mechanics of materials. During a machine learning stage, a classification of possible reduced order models is obtained through a clustering of loading environments by using simulation data. The recognition of the suitable reduced order model is performed via a convolutional neural network (CNN) applied to a digital image of the mechanical test. The CNN recommends a convenient mechanical model available in a dictionary of reduced order models. The output of the convolutional neural network being a model, an error estimator is proposed to assess the accuracy of this output. This talk will detail simple algorithmic choices that allowed a realistic mechanical modeling via computer vision.

B-366, Faculty of Civil Engineering, Thákurova 7, 166 29 Prague 6
Contact person
Martin Horák, martin.horak@fsv.cvut.cz
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