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The computational cost for high energy physics detector simulation in futureexperimental facilities is going to exceed the current available resources. Toovercome this challenge, new ideas on surrogate models using machine learningmethods are being explored to replace computationally expensive components.Additionally, differentiable programming has been proposed as a complementaryapproach, providing controllable and scalable simulation routines. In thisdocument, new and ongoing efforts for surrogate models and differentialprogramming applied to detector simulation are discussed in the context of the2021 Particle Physics Community Planning Exercise (`Snowmass').

New directions for surrogate models and differentiable programming for High Energy Physics detector simulation