Cai, YoushuaiLi, ChenSong, HaichuanXie, YouchenWang, ChangBoChristie, MarcHan, Ping-HsuanLin, Shih-SyunPietroni, NicoSchneider, TeseoTsai, Hsin-RueyWang, Yu-ShuenZhang, Eugene2025-10-072025-10-072025978-3-03868-295-0https://doi.org/10.2312/pg.20251265https://diglib.eg.org/handle/10.2312/pg20251265We propose DiffQN, an efficient differentiable quasi-Newton method for elastodynamics simulation, addressing the challenges of high computational cost and limited material generality in existing differentiable physics frameworks. Our approach employs a per-frame initial Hessian approximation and selectively delays Hessian updates, resulting in improved convergence and faster forward simulation compared to prior methods such as DiffPD. During backpropagation, we further reduce gradient evaluation costs by reusing prefactorized linear system solvers from the forward pass. Unlike previous approaches, our method supports a wide range of hyperelastic materials without restrictions on material energy functions, enabling the simulation of more general physical phenomena. To efficiently handle high-resolution systems with large degrees of freedom, we introduce a subspace optimization strategy that projects both forward simulation and backpropagation into a low-dimensional subspace, significantly improving computational and memory efficiency. Our subspace method can provide effective initial guesses for subsequent full-space optimization. We validate our framework on diverse applications, including system identification, initial state optimization, and facial animation, demonstrating robust performance and achieving up to 1.8× to 18.9× speedup over state-of-the-art methods.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Physical simulationComputing methodologies → Physical simulation10.2312/pg.2025126512 pages