Handling Iterative Solvers in an Algorithmic Differentiation Framework Using Implicit Methods

Jeffery Allen, Olga Doronina, Jon Maack, Ethan Young, Andrew Ning, Adam Cardoza, Eric Green

Research output: NRELPoster


Differentiable programming is a powerful concept as it enables the seemly propagation of gradients through functions, algorithms, and/or whole physics simulations. These gradients are useful for a wide variety of applications, including sensitivity studies and machine learning, but one of particular interest is optimization. Gradient-based optimization, enabled through automatic/algorithmic differentiation (AD), can be used on predictive physical models to efficiently optimize a set of design variables. AD methods are a particularly promising approach to complex physics simulations because they can be shown to scale well with an increasing number of design variables; however, care must be taken when coupling between different models or different states of a single model.
Original languageAmerican English
PublisherNational Renewable Energy Laboratory (NREL)
StatePublished - 2024

Publication series

NamePresented at the ASCR Computer Science Principal Investigators Meeting, 5-7 February 2024, Atlanta, Georgia

NREL Publication Number

  • NREL/PO-2C00-88610


  • algorithmic differentiation
  • differentiable programming
  • multi-physics
  • solvers
  • time-dependent


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