A review of automatic differentiation and its efficient implementation
CC Margossian - Wiley interdisciplinary reviews: data mining …, 2019 - Wiley Online Library
Derivatives play a critical role in computational statistics, examples being Bayesian
inference using Hamiltonian Monte Carlo sampling and the training of neural networks …
inference using Hamiltonian Monte Carlo sampling and the training of neural networks …
Effective adjoint approaches for computational fluid dynamics
The adjoint method is used for high-fidelity aerodynamic shape optimization and is an
efficient approach for computing the derivatives of a function of interest with respect to a …
efficient approach for computing the derivatives of a function of interest with respect to a …
Toward the end-to-end optimization of particle physics instruments with differentiable programming
T Dorigo, A Giammanco, P Vischia, M Aehle, M Bawaj… - Reviews in Physics, 2023 - Elsevier
The full optimization of the design and operation of instruments whose functioning relies on
the interaction of radiation with matter is a super-human task, due to the large dimensionality …
the interaction of radiation with matter is a super-human task, due to the large dimensionality …
Reverse-mode automatic differentiation and optimization of GPU kernels via Enzyme
Computing derivatives is key to many algorithms in scientific computing and machine
learning such as optimization, uncertainty quantification, and stability analysis. Enzyme is a …
learning such as optimization, uncertainty quantification, and stability analysis. Enzyme is a …
Enhancing the shear-stress-transport turbulence model with symbolic regression: A generalizable and interpretable data-driven approach
Turbulence modeling within the Reynolds-averaged Navier-Stokes (RANS) equations'
framework is essential in engineering due to its high efficiency. Field-inversion and machine …
framework is essential in engineering due to its high efficiency. Field-inversion and machine …
Field inversion and machine learning with embedded neural networks: Physics-consistent neural network training
JR Holland, JD Baeder, K Duraisamy - AIAA Aviation 2019 Forum, 2019 - arc.aiaa.org
The deficiencies of Reynolds averaged Navier-Stokes (RANS) models have been well
documented in a wide variety of practical applications. RANS models make use of …
documented in a wide variety of practical applications. RANS models make use of …
A discrete adjoint framework coupled with adaptive PCE for robust aerodynamic optimization of turbomachinery under flow uncertainty
J Zhang, L Li, X Dong, Z Zhang, Y Zhang… - Aerospace Science and …, 2023 - Elsevier
Flow uncertainty is commonly encountered in turbomachinery. To mitigate the negative
effects caused by the flow uncertainty, a framework coupled with adaptive polynomial chaos …
effects caused by the flow uncertainty, a framework coupled with adaptive polynomial chaos …
Auto: a framework for automatic differentiation in topology optimization
A critical step in topology optimization (TO) is finding sensitivities. Manual derivation and
implementation of sensitivities can be quite laborious and error-prone, especially for non …
implementation of sensitivities can be quite laborious and error-prone, especially for non …
ADAPT: Algorithmic differentiation applied to floating-point precision tuning
HPC applications use floating point arithmetic operations extensively to solve computational
problems. Mixed-precision computing seeks to use the lowest precision data type that is …
problems. Mixed-precision computing seeks to use the lowest precision data type that is …
Scalable automatic differentiation of multiple parallel paradigms through compiler augmentation
Derivatives are key to numerous science, engineering, and machine learning applications.
While existing tools generate derivatives of programs in a single language, modern parallel …
While existing tools generate derivatives of programs in a single language, modern parallel …