English(EN) Differentiate the Solver, Not the Equation: Reverse-Sweep Adjoints for Block Implicit Simulation

新方法实现复杂系统的可微分高效仿真

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-11 04:00

研究人员开发了一种新颖的仿真隐式求解器微分方法，称为求解器级微分。该方法利用块隐式更新的结构，以相反的顺序应用伴随更新，以镜像前向求解器的过程。这项技术在 Vertex Block Descent 上进行了演示，结果表明该可微分求解器比传统的展开式自动微分和方程级隐式微分更快、内存效率更高。 AI

影响能够更有效地计算复杂仿真的梯度，可能加速人工智能驱动的学习和控制系统的研究。

排序理由该条目是一篇学术论文，详细介绍了一种新的可微分仿真方法。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Lei Shu, Ying Jiang, Kui Wu, Yin Yang, Leonidas Guibas, Chenfanfu Jiang · 2026-08-11 04:00

区分求解器而非方程：块隐式仿真的反向扫掠伴随法

arXiv:2608.08559v1 Announce Type: cross Abstract: Differentiable simulation is a key component in learning, control, and inverse problems, where gradients through nonlinear implicit solvers are required. Existing approaches either rely on unrolled automatic differentiation, whose…