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Found 7 papers, 1 papers with code
Profiling checkpointing schedules in adjoint ST-AD
Checkpointing is a cornerstone of data-flow reversal in adjoint algorithmic differentiation.
Forward Gradients for Data-Driven CFD Wall Modeling
Computational Fluid Dynamics (CFD) is used in the design and optimization of gas turbines and many other industrial/ scientific applications.
Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models
Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean.
Understanding Automatic Differentiation Pitfalls
Automatic differentiation, also known as backpropagation, AD, autodiff, or algorithmic differentiation, is a popular technique for computing derivatives of computer programs accurately and efficiently.
Source-to-Source Automatic Differentiation of OpenMP Parallel Loops
This paper presents our work toward correct and efficient automatic differentiation of OpenMP parallel worksharing loops in forward and reverse mode.
Automatic Differentiation for Adjoint Stencil Loops
In this paper we present a novel combination of automatic differentiation and loop transformations that preserves the structure and memory access pattern of stencil loops, while computing fully consistent derivatives.
Architecture and performance of Devito, a system for automated stencil computation
Some of these are obtained through well-established stencil optimizers, integrated in the back-end of the Devito compiler.
Mathematical Software 65N06, 68N20
