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Comput. Ind. Biomed. Art"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Deep simulations have gained widespread attention owing to their excellent acceleration performances. However, these methods cannot provide effective collision detection and response strategies. We propose a deep interactive physical simulation framework that can effectively address tool-object collisions. The framework can predict the dynamic information by considering the collision state. In particular, the graph neural network is chosen as the base model, and a collision-aware recursive regression module is introduced to update the network parameters recursively using interpenetration distances calculated from the vertex-face and edge-edge tests. Additionally, a novel self-supervised collision term is introduced to provide a more compact collision response. This study extensively evaluates the proposed method and shows that it effectively reduces interpenetration artifacts while ensuring high simulation efficiency.<\/jats:p>","DOI":"10.1186\/s42492-022-00113-4","type":"journal-article","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T03:23:33Z","timestamp":1654572213000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Collision-aware interactive simulation using graph neural networks"],"prefix":"10.1186","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7988-2063","authenticated-orcid":false,"given":"Xin","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinling","family":"Qian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziliang","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pheng-Ann","family":"Heng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,7]]},"reference":[{"key":"113_CR1","doi-asserted-by":"publisher","unstructured":"Holden D, Duong BC, Datta S, Nowrouzezahrai D (2019) Subspace neural physics: Fast data-driven interactive simulation. 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