Learning Elastic Constitutive Material and Damping Models
| dc.contributor.author | Wang, Bin | en_US |
| dc.contributor.author | Deng, Yuanmin | en_US |
| dc.contributor.author | Kry, Paul | en_US |
| dc.contributor.author | Ascher, Uri | en_US |
| dc.contributor.author | Huang, Hui | en_US |
| dc.contributor.author | Chen, Baoquan | en_US |
| dc.contributor.editor | Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue | en_US |
| dc.date.accessioned | 2020-10-29T18:50:04Z | |
| dc.date.available | 2020-10-29T18:50:04Z | |
| dc.date.issued | 2020 | |
| dc.description.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. | en_US |
| dc.description.number | 7 | |
| dc.description.sectionheaders | Physics-based Material Animation | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 39 | |
| dc.identifier.doi | 10.1111/cgf.14128 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 81-91 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.14128 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf14128 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | Computing methodologies | |
| dc.subject | Collision detection | |
| dc.subject | Hardware | |
| dc.subject | Sensors and actuators | |
| dc.subject | PCB design and layout | |
| dc.title | Learning Elastic Constitutive Material and Damping Models | en_US |