Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Blühdorn, Johannes"'
Autor:
Blühdorn, Johannes, Gauger, Nicolas R.
In shared-memory parallel automatic differentiation, inputs that are shared among simultaneous thread-local preaccumulations lead to data races if Jacobians are accumulated with a single, shared vector of adjoint variables. In this work, we discuss t
Externí odkaz:
http://arxiv.org/abs/2405.07819
The open-source multiphysics suite SU2 features discrete adjoints by means of operator overloading automatic differentiation (AD). While both primal and discrete adjoint solvers support MPI parallelism, hybrid parallelism using both MPI and OpenMP ha
Externí odkaz:
http://arxiv.org/abs/2405.06056
Tools for algorithmic differentiation (AD) provide accurate derivatives of computer-implemented functions for use in, e. g., optimization and machine learning (ML). However, they often require the source code of the function to be available in a rest
Externí odkaz:
http://arxiv.org/abs/2212.13760
Algorithmic differentiation (AD) is a set of techniques that provide partial derivatives of computer-implemented functions. Such a function can be supplied to state-of-the-art AD tools via its source code, or via an intermediate representation produc
Externí odkaz:
http://arxiv.org/abs/2209.01895
Autor:
Aehle, Max, Alme, Johan, Barnaföldi, Gergely Gábor, Blühdorn, Johannes, Bodova, Tea, Borshchov, Vyacheslav, Brink, Anthony van den, Eikeland, Viljar, Feofilov, Gregory, Garth, Christoph, Gauger, Nicolas R., Grøttvik, Ola, Helstrup, Håvard, Igolkin, Sergey, Keidel, Ralf, Kobdaj, Chinorat, Kortus, Tobias, Kusch, Lisa, Leonhardt, Viktor, Mehendale, Shruti, Mulawade, Raju Ningappa, Odland, Odd Harald, O'Neill, George, Papp, Gábor, Peitzmann, Thomas, Pettersen, Helge Egil Seime, Piersimoni, Pierluigi, Pochampalli, Rohit, Protsenko, Maksym, Rauch, Max, Rehman, Attiq Ur, Richter, Matthias, Röhrich, Dieter, Sagebaum, Max, Santana, Joshua, Schilling, Alexander, Seco, Joao, Songmoolnak, Arnon, Sudár, Ákos, Tambave, Ganesh, Tymchuk, Ihor, Ullaland, Kjetil, Varga-Kofarago, Monika, Volz, Lennart, Wagner, Boris, Wendzel, Steffen, Wiebel, Alexander, Xiao, RenZheng, Yang, Shiming, Zillien, Sebastian
Objective. Algorithmic differentiation (AD) can be a useful technique to numerically optimize design and algorithmic parameters by, and quantify uncertainties in, computer simulations. However, the effectiveness of AD depends on how "well-linearizabl
Externí odkaz:
http://arxiv.org/abs/2202.05551
We present the new software OpDiLib, a universal add-on for classical operator overloading AD tools that enables the automatic differentiation (AD) of OpenMP parallelized code. With it, we establish support for OpenMP features in a reverse mode opera
Externí odkaz:
http://arxiv.org/abs/2102.11572
The identification of primal variables and adjoint variables is usually done via indices in operator overloading algorithmic differentiation tools. One approach is a linear management scheme, which is easy to implement and supports memory optimizatio
Externí odkaz:
http://arxiv.org/abs/2006.12992
We propose a universal method for the evaluation of generalized standard materials that greatly simplifies the material law implementation process. By means of automatic differentiation and a numerical integration scheme, AutoMat reduces the implemen
Externí odkaz:
http://arxiv.org/abs/2006.04391
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Blühdorn, Johannes1 (AUTHOR) johannes.bluehdorn@scicomp.uni-kl.de, Gauger, Nicolas R.1 (AUTHOR), Kabel, Matthias2 (AUTHOR)
Publikováno v:
Computational Mechanics. Feb2022, Vol. 69 Issue 2, p589-613. 25p.
