Results 1 to 10 of about 84,089 (265)
A Parameter Refinement Method for Ptychography Based on Deep Learning Concepts
Condensed Matter, 2021 X-ray ptychography is an advanced computational microscopy technique, which is delivering exceptionally detailed quantitative imaging of biological and nanotechnology specimens, which can be used for high-precision X-ray measurements.
Francesco Guzzi +4 more
doaj +1 more source
Automatic Differentiation in Prolog
Theory and Practice of Logic Programming, 2023 AbstractAutomatic differentiation (AD) is a range of algorithms to compute the numeric value of a function’s (partial) derivative, where the function is typically given as a computer program or abstract syntax tree. AD has become immensely popular as part of many learning algorithms, notably for neural networks. This paper uses Prolog to systematically
Schrijvers, Tom +2 more
openaire +5 more sources
St. Petersburg Polytechnical University Journal: Physics and Mathematics, 2023 In the paper, the two main approaches to calculating the Jacobian of the Navier–Stokes equations, namely, the continuum (CA) and discrete (DA) approaches, have been directly compared for the first time. The DA to calculating this Jacobian was implemented
Golubkov Valentin, Garbaruk Andrey
doaj +1 more source
Wide-Angular Tolerance Optical Filter Design and Its Application to Green Pepper Segmentation
Sensors, 2023 The optical filter is critical in many applications requiring wide-angle imaging perception. However, the transmission curve of the typical optical filter will change at an oblique incident angle due to the optical path of the incident light change.
Jun Yu, Shu Zhan, Toru Kurihara
doaj +1 more source
Mixed-language automatic differentiation [PDF]
Optimization Methods and Software, 2018 As Automatic Differentiation (AD) usage is spreading to larger and more sophisticated applications, problems arise for codes that use several programming languages. This work describes the issues involved in interoperability between languages and focuses on the main issue which is parameter passing.
Pascual, Valérie, Hascoët, Laurent
openaire +3 more sources
Selective Path Automatic Differentiation: Beyond Uniform Distribution on Backpropagation Dropout
IEEE Access, 2023 This paper introduces Selective Path Automatic Differentiation (SPAD), a novel approach to reducing memory consumption and mitigating overfitting in gradient-based models for embedded artificial intelligence.
Paul Peseux +3 more
doaj +1 more source
Introduction to Automatic Differentiation [PDF]
PAMM, 2003 AbstractAutomatic, or algorithmic, differentiation (AD) is a chain rule‐based technique for evaluating derivatives of functions given as computer programs for their elimination. We review the main characteristics and application of AD and illustrate the methodology on a simple example.
Griewank, Andreas, Walther, Andrea
openaire +2 more sources
Automatic Differentiation for Solid Mechanics [PDF]
Archives of Computational Methods in Engineering, 2020 30 pages, 9 figures, 2 appendices, accepted on Archives of Computational Methods in ...
Andrea Vigliotti, Ferdinando Auricchio
openaire +3 more sources
AutoMat: automatic differentiation for generalized standard materials on GPUs [PDF]
Computational Mechanics, 2021 AbstractWe 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 implementation effort to two potential functions. By moving AutoMat to the GPU, we close the performance
Johannes Blühdorn +2 more
openaire +4 more sources
Automatic differentiable numerical renormalization group
Physical Review Research, 2022 14 pages, 7 figures and 2 ...
Jonas B. Rigo, Andrew K. Mitchell
openaire +3 more sources