  The computational cost for high energy physics detector simulation in future
experimental facilities is going to exceed the current available resources. To
overcome this challenge, new ideas on surrogate models using machine learning
methods are being explored to replace computationally expensive components.
Additionally, differentiable programming has been proposed as a complementary
approach, providing controllable and scalable simulation routines. In this
document, new and ongoing efforts for surrogate models and differential
programming applied to detector simulation are discussed in the context of the
2021 Particle Physics Community Planning Exercise (`Snowmass').
