Akce ČVUT

 Dnes
«  únor  2019  »
Po Út St Čt So Ne
        1 2 3
4 5 6 7 8 9 10
11 12 13 14 15 16 17
18 19 20 21 22 23 24
25 26 27 28      

Zpět na kalendář

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

15.02.2019   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.

Místo konání
B-366, Faculty of Civil Engineering, Thákurova 7, 166 29 Prague 6
Kontaktní osoba
Martin Horák, martin.horak@fsv.cvut.cz
Podrobnější informace
http://bit.ly/2HIJvaU