Results 11 to 20 of about 87,618 (263)
Optimization of Transcription Factor Genetic Circuits
Biology, 2022 Transcription factors (TFs) affect the production of mRNAs. In essence, the TFs form a large computational network that controls many aspects of cellular function. This article introduces a computational method to optimize TF networks. The method extends
Steven A. Frank
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Physics-Based Deep Learning for Flow Problems
Energies, 2021 It is the tradition for the fluid community to study fluid dynamics problems via numerical simulations such as finite-element, finite-difference and finite-volume methods.
Yubiao Sun, Qiankun Sun, Kan Qin
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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
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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
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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
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PINNs algorithm and its application in geotechnical engineering
Yantu gongcheng xuebao, 2021 The physical information neural networks (PINNs) algorithm, a new mesh-free algorithm, uses the automatic differential method to embed the partial differential equation directly into the neural networks so as to realize the intelligent solution of the ...
LAN Peng 1, LI Hai-chao 1, YE Xin-yu 1, ZHANG Sheng 1, SHENG Dai-chao 1, 2
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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
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Quantum Optimal Control via Semi-Automatic Differentiation [PDF]
Quantum, 2022 We develop a framework of "semi-automatic differentiation" that combines existing gradient-based methods of quantum optimal control with automatic differentiation.
Michael H. Goerz +2 more
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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
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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
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