These days, most neural network frameworks are essentially collections of functions on multidimensional arrays with added support for a particular type of automatic differentiation (backpropagation). These features are generally useful for any task that requires numerical optimization (deep learning happens to be one of them) and, in this article, I want to show how these features can be particularly useful for physics-based simulation. I believe that being able to implement physics engines using deep learning frameworks can be very convenient to prototype new environments in the context of reinforcement learning. That is how the physics engine for this project I presented at NeurIPS 2019 was implemented. 
 链接地址： https://medium.com/swlh/physics-based-simulation-via-backpropagation-on-energy-functions-6d3b0e93f5fb