Department of Mechanics: Seminar: Abstract Ryckelynck 2019
Computer vision with error estimation for reduced-order modeling of macroscopic mechanical tests
David Ryckelynck, Professeur, Mines ParisTech, Centre des Matériaux
Department meeting room (B-366), Faculty of Civil Engineering, Czech Technical University in Prague
Friday, 15 February 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.
Further details on this work are available here.