  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.
