Learning Elastic Constitutive Material and Damping Models

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Date
2020
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The Eurographics Association and John Wiley & Sons Ltd.
Abstract
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.
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@article{
10.1111:cgf.14128
, journal = {Computer Graphics Forum}, title = {{
Learning Elastic Constitutive Material and Damping Models
}}, author = {
Wang, Bin
and
Deng, Yuanmin
and
Kry, Paul
and
Ascher, Uri
and
Huang, Hui
and
Chen, Baoquan
}, year = {
2020
}, publisher = {
The Eurographics Association and John Wiley & Sons Ltd.
}, ISSN = {
1467-8659
}, DOI = {
10.1111/cgf.14128
} }
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