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arxiv_cs_gr 2026年4月20日

分割と切り捨て:相互貫透・非侵入を免れ、逆転も排除する連成マルチフィールドシステム向けフレームワーク

Divide and Truncate: A Penetration and Inversion Free Framework for Coupled Multi-physics Systems

Translated: 2026/4/20 10:52:24
divide-and-truncatemulti-physicspenetration-freecollision-resolutionsimulation-framework

Japanese Translation

arXiv:2604.15513v1 Announce Type: new 要約:We present Divide and Truncate (DAT), a unified framework for coupling multi-physics systems through penetration-free collision handling, including rigid bodies, volumetric soft bodies, thin shells, rods, and animated objects. By partitioning the ambient space into exclusive regions and truncating displacements to remain within them, DAT guarantees penetration-free contact resolution. Our \\emph{Planar-DAT} variant further refines this by restricting only motion toward nearby surfaces, leaving tangential movement unconstrained, which addresses the artificial damping and deadlock problems of previous works. The framework is material-agnostic: each object responds to contact without knowledge of the opposing body's physics. Our method is also solver-agnostic; it can be integrated seamlessly with any iterative optimizer as a post-processing step, enabling robust simulation of complex multi-body interactions.

Original Content

arXiv:2604.15513v1 Announce Type: new Abstract: We present Divide and Truncate (DAT), a unified framework for coupling multi-physics systems through penetration-free collision handling, including rigid bodies, volumetric soft bodies, thin shells, rods, and animated objects. By partitioning the ambient space into exclusive regions and truncating displacements to remain within them, DAT guarantees penetration-free contact resolution. Our \emph{Planar-DAT} variant further refines this by restricting only motion toward nearby surfaces, leaving tangential movement unconstrained, which addresses the artificial damping and deadlock problems of previous works. The framework is material-agnostic: each object responds to contact without knowledge of the opposing body's physics. Our method is also solver-agnostic; it can be integrated seamlessly with any iterative optimizer as a post-processing step, enabling robust simulation of complex multi-body interactions.