发表机构
Zhongguancun Academy; Zhongguancun Institute of Artificial Intelligence（中关村学院; 中关村人工智能研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文构建12个可微分物理模拟器及相应逆问题，基准测试多种优化器性能，为可微分编程中优化器的选择与设计提供指导。
AI 中文摘要
使用可微分物理模拟器求解逆问题有望彻底改变科学发现和工程设计，因为它既享有严格数值物理模拟器带来的物理正确性，又享有自动微分和基于梯度的优化带来的高效率和有效性。然而，目前这种范式在优化方面面临性能问题。在这项工作中，我们旨在对不同优化器在求解各种逆问题时的性能进行基准测试。我们构建了12个可微分物理模拟器，涵盖离散力学、连续力学、原子模拟、渲染和半经验物理模型等物理领域。基于这些模拟器，我们设计了相应的逆问题，可分为参数识别、逆设计和最优控制。最后，我们进行了大量实验，比较不同优化器（包括常规一阶方法、近似二阶方法以及全局优化器）在这些逆问题上的性能，并分析结果，为如何选择和设计可微分编程的优化器提供见解。我们希望这样的基准测试能够激发更有效优化器的发展，并进一步推动可微分编程在各个科学和工程领域的应用。
英文摘要
Solving inverse problems with differentiable physics simulators holds the potential to revolutionize scientific discovery and engineering design, as it enjoys both the strict physical correctness from rigorous numerical physics simulators, and the high efficiency and effectiveness from automatic differentiation and gradient-based optimization. However, currently, this paradigm faces performance issues in optimization. In this work, we target benchmarking the performance of different optimizers to solve various inverse problems. We construct 12 differentiable physics simulators spanning physics domains including discrete mechanics, continuous mechanics, atomistic simulations, rendering, and semi-empirical physics models. Based on these simulators, we design corresponding inverse problems that can be categorized into parameter identification, inverse design, and optimal control. Finally, we conduct extensive experiments to compare the performance of different optimizers, including regular first-order methods, approximate second-order methods, as well as global optimizers, on these inverse problems, and analyze the results to provide insights on how to choose and design optimizers for differentiable programming. We hope such benchmarks can inspire the development of more effective optimizers, and further promote the applications of differentiable programming in various scientific and engineering domains.
