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Graph."],"published-print":{"date-parts":[[2022,4,30]]},"abstract":"<jats:p>\n            We present a novel, fast differentiable simulator for soft-body learning and control applications. Existing differentiable soft-body simulators can be classified into two categories based on their time integration methods: Simulators using explicit timestepping schemes require tiny timesteps to avoid numerical instabilities in gradient computation, and simulators using implicit time integration typically compute gradients by employing the adjoint method and solving the expensive linearized dynamics. Inspired by\n            <jats:bold>Projective Dynamics<\/jats:bold>\n            (\n            <jats:bold>PD<\/jats:bold>\n            ), we present\n            <jats:bold>Differentiable Projective Dynamics<\/jats:bold>\n            (\n            <jats:bold>DiffPD<\/jats:bold>\n            ), an efficient differentiable soft-body simulator based on PD with implicit time integration. The key idea in DiffPD is to speed up backpropagation by exploiting the prefactorized Cholesky decomposition in forward PD simulation. In terms of contact handling, DiffPD supports two types of contacts: a penalty-based model describing contact and friction forces and a complementarity-based model enforcing non-penetration conditions and static friction. We evaluate the performance of DiffPD and observe it is 4\u201319 times faster compared with the standard Newton\u2019s method in various applications including system identification, inverse design problems, trajectory optimization, and closed-loop control. We also apply DiffPD in a\n            <jats:bold>reality-to-simulation<\/jats:bold>\n            (\n            <jats:bold>real-to-sim<\/jats:bold>\n            ) example with contact and collisions and show its capability of reconstructing a digital twin of real-world scenes.\n          <\/jats:p>","DOI":"10.1145\/3490168","type":"journal-article","created":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T01:32:58Z","timestamp":1638235978000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":77,"title":["DiffPD: Differentiable Projective Dynamics"],"prefix":"10.1145","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7337-7667","authenticated-orcid":false,"given":"Tao","family":"Du","sequence":"first","affiliation":[{"name":"MIT CSAIL, Cambridge, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3326-7943","authenticated-orcid":false,"given":"Kui","family":"Wu","sequence":"additional","affiliation":[{"name":"MIT CSAIL, Cambridge, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pingchuan","family":"Ma","sequence":"additional","affiliation":[{"name":"MIT CSAIL, Cambridge, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastien","family":"Wah","sequence":"additional","affiliation":[{"name":"MIT CSAIL, Cambridge, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Spielberg","sequence":"additional","affiliation":[{"name":"MIT CSAIL, Cambridge, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniela","family":"Rus","sequence":"additional","affiliation":[{"name":"MIT CSAIL, Cambridge, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wojciech","family":"Matusik","sequence":"additional","affiliation":[{"name":"MIT CSAIL, Cambridge, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,11,29]]},"reference":[{"key":"e_1_3_3_2_1","unstructured":"J\u00e9r\u00e9mie Allard St\u00e9phane Cotin Fran\u00e7ois Faure Pierre-Jean Bensoussan Fran\u00e7ois Poyer Christian Duriez Herv\u00e9 Delingette and Laurent Grisoni. 2007. \u2013An open source framework for medical simulation. 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