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 …
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 …
The deal. II library, version 9.2
The deal.II library, Version 9.2 Skip to content Should you have institutional access? Here's how
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Automatic differentiation in machine learning: a survey
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine
learning. Automatic differentiation (AD), also called algorithmic differentiation or simply" auto …
learning. Automatic differentiation (AD), also called algorithmic differentiation or simply" auto …
JuMP: A modeling language for mathematical optimization
JuMP is an open-source modeling language that allows users to express a wide range of
optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and …
optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and …
The deal. II finite element library: Design, features, and insights
Abstract deal. II is a state-of-the-art finite element library focused on generality, dimension-
independent programming, parallelism, and extensibility. Herein, we outline its primary …
independent programming, parallelism, and extensibility. Herein, we outline its primary …
TMB: automatic differentiation and Laplace approximation
TMB is an open source R package that enables quick implementation of complex nonlinear
random effect (latent variable) models in a manner similar to the established AD Model …
random effect (latent variable) models in a manner similar to the established AD Model …
The Tapenade automatic differentiation tool: principles, model, and specification
L Hascoet, V Pascual - ACM Transactions on Mathematical Software …, 2013 - dl.acm.org
Tapenade is an Automatic Differentiation (AD) tool which, given a Fortran or C code that
computes a function, creates a new code that computes its tangent or adjoint derivatives …
computes a function, creates a new code that computes its tangent or adjoint derivatives …
[HTML][HTML] The open porous media flow reservoir simulator
AF Rasmussen, TH Sandve, K Bao, A Lauser… - … & Mathematics with …, 2021 - Elsevier
Abstract The Open Porous Media (OPM) initiative is a community effort that encourages
open innovation and reproducible research for simulation of porous media processes. OPM …
open innovation and reproducible research for simulation of porous media processes. OPM …
The deal. II library, version 9.0
The deal.II library, Version 9.0 Skip to content Should you have institutional access? Here's how
to get it ... De Gruyter € EUR - Euro £ GBP - Pound $ USD - Dollar EN English Deutsch 0 …
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