Results 11 to 20 of about 84,089 (265)
Automatic Differentiation for Inverse Problems in X-ray Imaging and Microscopy [PDF]
Life, 2023 Computational techniques allow breaking the limits of traditional imaging methods, such as time restrictions, resolution, and optics flaws. While simple computational methods can be enough for highly controlled microscope setups or just for previews, an ...
Francesco Guzzi +4 more
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MapsTorch: automatic differentiation for X-ray fluorescence data analysis [PDF]
Journal of Synchrotron RadiationX-ray fluorescence (XRF) is a popular spectroscopy technique for elemental analysis. Spectrum fitting and parameter tuning are at the core of XRF analysis and are conventionally manually intensive, especially for synchrotron experiments involving large ...
Xiangyu Yin +5 more
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Acoustic hologram optimisation using automatic differentiation. [PDF]
Sci Rep, 2021 AbstractAcoustic holograms are the keystone of modern acoustics. They encode three-dimensional acoustic fields in two dimensions, and their quality determines the performance of acoustic systems. Optimisation methods that control only the phase of an acoustic wave are considered inferior to methods that control both the amplitude and phase of the wave.
Fushimi T, Yamamoto K, Ochiai Y.
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Wave-function positivization via automatic differentiation [PDF]
Physical Review Research, 2020 We introduce a procedure to systematically search for a local unitary transformation that maps a wave function with a nontrivial sign structure into a positive-real form. The transformation is parametrized as a quantum circuit compiled into a set of one-
Giacomo Torlai +4 more
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Automatic differentiation in PCF [PDF]
Proceedings of the ACM on Programming Languages, 2021 We study the correctness of automatic differentiation (AD) in the context of a higher-order, Turing-complete language (PCF with real numbers), both in forward and reverse mode. Our main result is that, under mild hypotheses on the primitive functions included in the language, AD is almost everywhere correct, that is, it computes the ...
Mazza, Damiano, Pagani, Michele
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Automatic Differentiation in ROOT [PDF]
EPJ Web of Conferences, 2020 In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evaluate the derivative of a function specified by a computer program.
Vassilev Vassil +2 more
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Application of Generalized (Hyper-) Dual Numbers in Equation of State Modeling
Frontiers in Chemical Engineering, 2021 The calculation of derivatives is ubiquitous in science and engineering. In thermodynamics, in particular, state properties can be expressed as derivatives of thermodynamic potentials. The manual differentiation of complex models can be tedious and error-
Philipp Rehner, Gernot Bauer
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Application of seeding and automatic differentiation in a large scale ocean circulation model [PDF]
Modeling, Identification and Control, 2005 Computation of the Jacobian in a 3-dimensional general ocean circulation model is considered in this paper. The Jacobian matrix considered in this paper is square, large and sparse.
Frode Martinsen, Dag Slagstad
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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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