Data-Free Learning of Reduced-Order Kinematics-Reference-Cited by-同舟云学术

Data-Free Learning of Reduced-Order Kinematics

Published:2023-07-23 Issue: Volume: Page:
ISSN:
Container-title:Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Proceedings
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Author:

Sharp Nicholas¹^ORCID,Romero Cristian²^ORCID,Jacobson Alec³^ORCID,Vouga Etienne⁴^ORCID,Kry Paul⁵^ORCID,Levin David I.W.⁶^ORCID,Solomon Justin⁷^ORCID

Affiliation:

1. University of Toronto, United States of America and NVIDIA, United States of America

2. Universidad Rey Juan Carlos, Spain

3. University of Toronto, Canada and Adobe Research, Canada

4. The University of Texas at Austin, United States of America

5. McGill University, Canada

6. University of Toronto, Canada and NVIDIA, Canada

7. Massachusetts Institute of Technology, United States of America

Funder

NSF (National Science Foundation)

NSERC

Air Force Office of Scientific Research

Army Research Office

Publisher

ACM

Reference51 articles.

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3. Real-Time subspace integration for St. Venant-Kirchhoff deformable models

4. P. Benner S. Gugercin and K. Willcox. 2015. A survey of projection-based model reduction methods for parametric dynamical systems. SIAM review 57 4 (2015) 483–531. P. Benner S. Gugercin and K. Willcox. 2015. A survey of projection-based model reduction methods for parametric dynamical systems. SIAM review 57 4 (2015) 483–531.

5. PBNS

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