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Relevant bibliographies by topics / Automatic Differentiation (AD)

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Academic literature on the topic 'Automatic Differentiation (AD)'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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Journal articles on the topic "Automatic Differentiation (AD)"

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Vassilev, Vassil, Aleksandr Efremov, and Oksana Shadura. "Automatic Differentiation in ROOT." EPJ Web of Conferences 245 (2020): 02015. http://dx.doi.org/10.1051/epjconf/202024502015.

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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. AD exploits the fact that every computer program, no matter how complicated, executes a sequence of elementary arithmetic operations (addition, subtraction, multiplication, division, etc.), elementary functions (exp, log, sin, cos, etc.) and control flow statements. AD takes source code of a function as input and produces source code of the derived function. By applying the chain rule repeatedly to these operations, derivatives of

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Thobirin, Aris, and Iwan Tri Riyadi Yanto. "Automatic differentiation based for particle swarm optimization Steepest descent direction." International Journal of Advances in Intelligent Informatics 1, no. 2 (2015): 90. http://dx.doi.org/10.26555/ijain.v1i2.29.

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Particle swam optimization (PSO) is one of the most effective optimization methods to find the global optimum point. In other hand, the descent direction (DD) is the gradient based method that has the local search capability. The combination of both methods is promising and interesting to get the method with effective global search capability and efficient local search capability. However, In many application, it is difficult or impossible to obtain the gradient exactly of an objective function. In this paper, we propose Automatic differentiation (AD) based for PSODD. we compare our methods on

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Li, Ming, Junqiang Bai, and Feng Qu. "Radar Cross Section Reduction and Shape Optimization using Adjoint Method and Automatic Differentiation." Applied Computational Electromagnetics Society 36, no. 3 (2021): 320–35. http://dx.doi.org/10.47037/2020.aces.j.360312.

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An efficient Radar Cross Section (RCS) gradient evaluation method based on the adjoint method is presented. The Method of Moments is employed to solve the Combined Field Integral Equation (CFIE) and the corresponding derivatives computing routines are generated by the program transformation Automatic Differentiation (AD) technique. The differential code is developed using three kinds of AD mode: tangent mode, multidirectional tangent mode, and adjoint mode. The differential code in adjoint mode is modified and optimized by changing the “two-sweeps” architecture into the “inner-loop two-sweeps”

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Li, Lianfa. "Optimal Inversion of Conversion Parameters from Satellite AOD to Ground Aerosol Extinction Coefficient Using Automatic Differentiation." Remote Sensing 12, no. 3 (2020): 492. http://dx.doi.org/10.3390/rs12030492.

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Satellite aerosol optical depth (AOD) plays an important role for high spatiotemporal-resolution estimation of fine particulate matter with diameters ≤2.5 μm (PM2.5). However, the MODIS sensors aboard the Terra and Aqua satellites mainly measure column (integrated) AOD using the aerosol (extinction) coefficient integrated over all altitudes in the atmosphere, and column AOD is less related to PM2.5 than low-level or ground-based aerosol (extinction) coefficient (GAC). With recent development of automatic differentiation (AD) that has been widely applied in deep learning, a method using AD to f

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Cajanus, Antti, Anette Hall, Juha Koikkalainen, et al. "Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis." Dementia and Geriatric Cognitive Disorders Extra 8, no. 1 (2018): 51–59. http://dx.doi.org/10.1159/000486849.

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Aims: We assessed the value of automated MRI quantification methods in the differential diagnosis of behavioral-variant frontotemporal dementia (bvFTD) from Alzheimer disease (AD), Lewy body dementia (LBD), and subjective memory complaints (SMC). We also examined the role of the C9ORF72-related genetic status in the differentiation sensitivity. Methods: The MRI scans of 50 patients with bvFTD (17 C9ORF72 expansion carriers) were analyzed using 6 quantification methods as follows: voxel-based morphometry (VBM), tensor-based morphometry, volumetry (VOL), manifold learning, grading, and white-mat

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Faure, Christèle, and Isabelle Charpentier. "Comparing Global Strategies for Coding Adjoints." Scientific Programming 9, no. 1 (2001): 1–10. http://dx.doi.org/10.1155/2001/485915.

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From a computational point of view, sensitivity analysis, calibration of a model, or variational data assimilation may be tackled after the differentiation of the numerical code representing the model into an adjoint code. This paper presents and compares methodologies to generate discrete adjoint codes. These methods can be implemented when hand writing adjoint codes, or within Automatic Differentiation (AD) tools. AD has been successfully applied to industrial codes that were large and general enough to fully validate this new technology. We compare these methodologies in terms of execution

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Konig, Alexandra, Aharon Satt, Alex Sorin, et al. "Use of Speech Analyses within a Mobile Application for the Assessment of Cognitive Impairment in Elderly People." Current Alzheimer Research 15, no. 2 (2018): 120–29. http://dx.doi.org/10.2174/1567205014666170829111942.

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Background: Various types of dementia and Mild Cognitive Impairment (MCI) are manifested as irregularities in human speech and language, which have proven to be strong predictors for the disease presence and progress ion. Therefore, automatic speech analytics provided by a mobile application may be a useful tool in providing additional indicators for assessment and detection of early stage dementia and MCI. Method: 165 participants (subjects with subjective cognitive impairment (SCI), MCI patients, Alzheimer's disease (AD) and mixed dementia (MD) patients) were recorded with a mobile applicati

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Fournier, David A., Hans J. Skaug, Johnoel Ancheta, et al. "AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models." Optimization Methods and Software 27, no. 2 (2012): 233–49. http://dx.doi.org/10.1080/10556788.2011.597854.

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Souhar, Otmane, and Jean-Baptiste Faure. "Approach for uncertainty propagation and design in Saint Venant equations via automatic sensitive derivatives applied to Saar river." Canadian Journal of Civil Engineering 36, no. 7 (2009): 1144–54. http://dx.doi.org/10.1139/l09-057.

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This paper describes the assessment of uncertainties of computational fluid dynamics (CFD) for modelling free surface flows. A series of CFD simulations, using MAillé GEnéralisé (MAGE), are employed to compute the flood extent resulting from the overflow of rivers. These simulated outputs are affected by uncertainties in the empiric roughness coefficients. Uncertainty propagation in MAGE outputs is difficult to evaluate because of the complexity and the nonlinearity of models. Assessment of uncertainties may be carried out by computing derivatives of the output results with respect to the inpu

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Vadakkepatt, Ajay, Sanjay R. Mathur, and Jayathi Y. Murthy. "Efficient automatic discrete adjoint sensitivity computation for topology optimization – heat conduction applications." International Journal of Numerical Methods for Heat & Fluid Flow 28, no. 2 (2018): 439–71. http://dx.doi.org/10.1108/hff-01-2017-0011.

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Purpose Topology optimization is a method used for developing optimized geometric designs by distributing material pixels in a given design space that maximizes a chosen quantity of interest (QoI) subject to constraints. The purpose of this study is to develop a problem-agnostic automatic differentiation (AD) framework to compute sensitivities of the QoI required for density distribution-based topology optimization in an unstructured co-located cell-centered finite volume framework. Using this AD framework, the authors develop and demonstrate the topology optimization procedure for multi-dimen

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Dissertations / Theses on the topic "Automatic Differentiation (AD)"

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Kang, Yixiu. "Implementation of Forward and Reverse Mode Automatic Differentiation for GNU Octave Applications." Ohio University / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1049467867.

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Book chapters on the topic "Automatic Differentiation (AD)"

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Tadjouddine, Mohamed, Shaun A. Forth, and John D. Pryce. "AD Tools and Prospects for Optimal AD in CFD Flux Jacobian Calculations." In Automatic Differentiation of Algorithms. Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0075-5_30.

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Giering, Ralf, and Thomas Kaminski. "Recomputations in Reverse Mode AD." In Automatic Differentiation of Algorithms. Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0075-5_33.

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Tadjouddine, Emmanuel M. "On Formal Certification of AD Transformations." In Advances in Automatic Differentiation. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68942-3_3.

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Caillau, Jean-Baptiste, and Joseph Noailles. "Continuous Optimal Control Sensitivity Analysis with AD." In Automatic Differentiation of Algorithms. Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0075-5_11.

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Tijskens, Engelbert, Herman Ramon, and Josse De Baerdemaeker. "Efficient Operator Overloading AD for Solving Nonlinear PDEs." In Automatic Differentiation of Algorithms. Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0075-5_19.

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Ben-Haj-Yedder, Adel, Eric Cances, and Claude Le Bris. "Optimal Laser Control of Chemical Reactions Using AD." In Automatic Differentiation of Algorithms. Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0075-5_24.

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Haase, Gundolf, Ulrich Langer, Ewald Lindner, and Wolfram Mühlhuber. "Optimal Sizing of Industrial Structural Mechanics Problems Using AD." In Automatic Differentiation of Algorithms. Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0075-5_21.

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Flanders, Harley. "Application of AD to a Family of Periodic Functions." In Automatic Differentiation of Algorithms. Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0075-5_38.

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Dignath, Florian, Peter Eberhard, and Axel Fritz. "Analytical Aspects and Practical Pitfalls in Technical Applications of AD." In Automatic Differentiation of Algorithms. Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0075-5_14.

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Abate, Jason, Steve Benson, Lisa Grignon, Paul Hovland, Lois McInnes, and Boyana Norris. "Integrating AD with Object-Oriented Toolkits for High-Performance Scientific Computing." In Automatic Differentiation of Algorithms. Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0075-5_20.

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Conference papers on the topic "Automatic Differentiation (AD)"

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Lin, Tsung-Chieh. "Linearization of Multibody Dynamic Systems Using Automatic Differentiation (AD) Tools." In ASME 1993 Design Technical Conferences. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/detc1993-0287.

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Abstract This paper presents an automatic method to linearize the dynamics of multibody systems that are modeled through a recursive approach. The first-order approximation of the nonlinear dynamic systems is obtained by the use of an automatic differentiation (AD) tool (GRESS) and a 9,700 lines Fortran model for the dynamics. The efficiency and accuracy of this AD implementation is shown by two examples: a five-bar closed-chain robot manipulator and a 18 degrees of freedom tractor-trailer. This study successfully demonstrates how to create a general-purpose numerical tool that can provide acc

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Pugazhendhi, K., and A. K. Dhingra. "Reliability Based Design Optimization Using Automatic Differentiation." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-65912.

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Typically, a reliability based design optimization (RBDO) problem is solved as a nested optimization problem because an evaluation of the probabilistic constraint(s) involves solving a minimization problem. Over the years, a number of algorithms have been developed to solve the RBDO problem efficiently. All of these approaches involve an evaluation of derivatives of the responses. In this paper, a decoupled approach using automatic differentiation (AD) is presented to solve the RBDO problem. The proposed approach employs AD to evaluate the reliability, as well as to evaluate the sensitivity of

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Zhang, Wei, Dingxi Wang, Xiuquan Huang, Tianxiao Yang, Hong Yan, and Jianling Li. "On the Use of the Automatic Differentiation for Developing a Linear Harmonic Solver." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-76004.

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The linear and nonlinear harmonic methods are efficient frequency domain methods for analyzing time periodic unsteady flow fields. They have been widely used in both academia and industry. But the cost and complexity of developing a linear harmonic solver has been limiting its wider applications. On the other hand, the automatic differentiation (AD) has long been used in the CFD community with a focus on generating adjoint codes in a reverse mode. All those AD tools can do a much better job in generating linearized codes in a tangent mode, but so far very little, if any, attention is paid to u

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Baras, John S., Vahid Tabatabaee, George Papageorgiou, and Nicolas Rentz. "Modelling and optimization for multi-hop wireless networks using fixed point and automatic differentiation." In 2008 6th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks and Workshops (WiOPT). IEEE, 2008. http://dx.doi.org/10.1109/wiopt.2008.4586081.

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