YASPS: A Symbolic Framework for Extensible, High-Performance IPC Simulation
Xuan Tang, Kemeng Huang, Gilbert Bernstein, Minchen Li, Tzumao Li
Abstract
Incremental Potential Contact (IPC) has emerged as a robust and unifying formulation for contact-rich physical simulation by casting elasticity and collision handling as a single energy minimization problem. Achieving high performance, however, typically requires heavily specialized implementations that hard-code assumptions about energies, primitive types, and parameterizations, creating a major barrier to extensibility. Adding new energies or alternative parameterizations often requires re-deriving first and second-order derivatives, and implementing new assembly logic for the global Hessian and gradient. This challenge is further exacerbated by collision energies, where the same energy definition is often applied to mixed parameterizations, which can lead to a combinatorial explosion of parameterization-specific derivative and assembly cases. In this paper we introduce YASPS, a framework for physical simulation that resolves this limitation by making structural relationships explicit in a differentiable representation. YASPS introduces two relational operators, JOIN and UNION, which encode connectivity and heterogeneous parameterizations directly in the symbolic computation graph. Using symbolic differentiation over these operators, YASPS automatically derives local derivatives, and determines the induced sparsity and block structure of global gradients and Hessians from the same description while avoiding any code explosion induced by mixed-parameterizations. Targeting IPC workloads, YASPS compiles the resulting symbolic graphs into GPU kernels for local energy evaluation, derivative computation, and block-sparse matrix assembly, and solves the resulting Newton systems using a GPU-based iterative solver. This approach achieves performance competitive with state-of-the-art IPC implementations while enabling new energies and parameterizations to be added through localized symbolic definitions, without hand-written derivative or assembly code.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext e50ba9f5-8052-4bf8-82a7-927ce861a692Builds on9
- DiffTaichi: Differentiable Programming for Physical SimulationYuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun et al.ICLR 2020 · 479 citations
- Incremental potential contact: intersection-and inversion-free, large-deformation dynamicsMinchen Li, Zachary Ferguson, Teseo Schneider, Timothy R. Langlois et al.SIGGRAPH 2020 · 320 citations
- Codimensional incremental potential contactMinchen Li, Danny M. Kaufman, Chenfanfu JiangSIGGRAPH 2021 · 117 citations
- Intersection-free rigid body dynamicsZachary Ferguson, Minchen Li, Teseo Schneider, Francisca Gil Ureta et al.SIGGRAPH 2021 · 83 citations
- Affine body dynamics: fast, stable and intersection-free simulation of stiff materialsLei Lan, Danny M. Kaufman, Minchen Li, Chenfanfu Jiang et al.SIGGRAPH 2022 · 46 citations
Related papers
- Penetration-free projective dynamics on the GPULei Lan, Guanqun Ma, Yin Yang, Changxi Zheng et al.SIGGRAPH 2022 · 49 citations
- Medial IPC: accelerated incremental potential contact with medial elasticsLei Lan, Yin Yang, Danny M. Kaufman, Junfeng Yao et al.SIGGRAPH 2021 · 41 citations
- JGS2: Near Second-order Converging Jacobi/Gauss-Seidel for GPU ElastodynamicsLei Lan, Zixuan Lu, Chun Yuan, Weiwei Xu et al.SIGGRAPH 2025 · 5 citations
- Heterogeneous Subspace Corrections for GPU Deformable Multibody DynamicsDewen Guo, Zhendong Wang, Minchen Li, Sheng Li et al.SIGGRAPH 2026
- Locality-Aware Automatic Differentiation on the GPU for Mesh-Based ComputationsAhmed H. Mahmoud, Rahul Goel, Jonathan Ragan-Kelley, Justin SolomonSIGGRAPH 2026
