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Learning Elastic Constitutive Material and Damping Models
Author(s) -
Wang Bin,
Deng Yuanmin,
Kry Paul,
Ascher Uri,
Huang Hui,
Chen Baoquan
Publication year - 2020
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.14128
Subject(s) - computer science , nonlinear system , inference , position (finance) , constraint (computer aided design) , parametric statistics , set (abstract data type) , cover (algebra) , object (grammar) , deformation (meteorology) , algorithm , artificial intelligence , mathematics , geometry , physics , mechanical engineering , statistics , finance , quantum mechanics , engineering , economics , programming language , meteorology
Commonly used linear and nonlinear constitutive material models in deformation simulation contain many simplifications and only cover a tiny part of possible material behavior. In this work we propose a framework for learning customized models of deformable materials from example surface trajectories. The key idea is to iteratively improve a correction to a nominal model of the elastic and damping properties of the object, which allows new forward simulations with the learned correction to more accurately predict the behavior of a given soft object. Space‐time optimization is employed to identify gentle control forces with which we extract necessary data for model inference and to finally encapsulate the material correction into a compact parametric form. Furthermore, a patch based position constraint is proposed to tackle the challenge of handling incomplete and noisy observations arising in real‐world examples. We demonstrate the effectiveness of our method with a set of synthetic examples, as well with data captured from real world homogeneous elastic objects.