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DTSTAMP:20220812T074334Z
LOCATION:Boston 3 Room
DTSTART;TZID=Europe/Stockholm:20220627T160000
DTEND;TZID=Europe/Stockholm:20220627T163000
UID:submissions.pasc-conference.org_PASC22_sess138_msa108@linklings.com
SUMMARY:Efficient Unsupervised Constitutive Law Identification and Discove
 ry
DESCRIPTION:Minisymposium\n\nEfficient Unsupervised Constitutive Law Ident
 ification and Discovery\n\nFlaschel, Kumar, De Lorenzis\n\nWe propose a ne
 w approach for data-driven automated discovery of constitutive laws in con
 tinuum mechanics. The approach is unsupervised, i.e., it requires no stres
 s data but only displacement and global force data, which can be realistic
 ally obtained from mechanical testing and digital image or volume correlat
 ion techniques; it can deliver either interpretable models, i.e., models t
 hat are embodied by parsimonious mathematical expressions, or black-box mo
 dels encoded in artificial neural networks; it is one-shot, i.e., discover
 y only needs one experiment in principle - but can use more if available. 
 The machine learning tools which enable discovery are sparse regression, l
 eading to the automatic selection of a few relevant entries from a potenti
 ally very large model space, as well as Bayesian regression, which allows 
 for the discovery of several constitutive laws along with their probabilit
 ies. After discussing the basics of the methodology, the talk illustrates 
 its first applications to hyperelasticity and plasticity, using both artif
 icial and experimental data, and highlights the ongoing work on further ap
 plications.\n\nDomain: Chemistry and Materials, Computer Science and Appli
 ed Mathematics, Engineering
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