Differentiable simulators continue to push the state of the art across a range of domains including computational physics, robotics, and machine learning. Their main value is the ability to compute gradients of physical processes, which allows differentiable simulators to be readily integrated into commonly employed gradient-based optimization schemes. To achieve this, a number of design decisions need to be considered representing trade-offs in versatility, computational speed, and accuracy of the gradients obtained. This paper presents an in-depth review of the evolving landscape of differentiable physics simulators. We introduce the foundations and core components of differentiable simulators alongside common design choices. This is followed by a practical guide and overview of open-source differentiable simulators that have been used across past research. Finally, we review and contextualize prominent applications of differentiable simulation. By offering a comprehensive review of the current state-of-the-art in differentiable simulation, this work aims to serve as a resource for researchers and practitioners looking to understand and integrate differentiable physics within their research. We conclude by highlighting current limitations as well as providing insights into future directions for the field.

翻译：可微分模拟器持续推动计算物理学、机器人学和机器学习等多个领域的前沿发展。其核心价值在于能够计算物理过程的梯度，这使得可微分模拟器能够直接融入广泛使用的基于梯度的优化框架。为实现这一目标，需要权衡考虑多种设计决策，涉及通用性、计算速度与所得梯度精度之间的平衡。本文深入评述了可微分物理模拟器的发展脉络，系统阐述了可微分模拟器的理论基础与核心组件，并分析了常见的设计选择。随后提供了实践指南，并综述了过往研究中使用的开源可微分模拟器。最后，我们对可微分模拟的典型应用进行了评述与背景分析。通过对可微分模拟当前前沿进展的全面梳理，本工作旨在为希望理解和整合可微分物理方法的研究者与实践者提供参考资源。文章最后指出了当前存在的局限性，并对该领域的未来发展方向提出了见解。