Literatura académica sobre el tema "GRADIENT COMPUTATION"
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Artículos de revistas sobre el tema "GRADIENT COMPUTATION"
Ding, Zhiyan y Qin Li. "Constrained Ensemble Langevin Monte Carlo". Foundations of Data Science 4, n.º 1 (2022): 37. http://dx.doi.org/10.3934/fods.2021034.
Texto completoB N, Shobha, Govind R. Kadambi, S. R. Shankapal y Yuri Vershinim. "Effect of variation in colour gradient information for optic flow computations". International Journal of Engineering & Technology 3, n.º 4 (17 de septiembre de 2014): 445. http://dx.doi.org/10.14419/ijet.v3i4.2722.
Texto completoSengupta, B., K. J. Friston y W. D. Penny. "Efficient gradient computation for dynamical models". NeuroImage 98 (septiembre de 2014): 521–27. http://dx.doi.org/10.1016/j.neuroimage.2014.04.040.
Texto completoXu, Jingyan y Frederic Noo. "Efficient gradient computation for optimization of hyperparameters". Physics in Medicine & Biology 67, n.º 3 (7 de febrero de 2022): 03NT01. http://dx.doi.org/10.1088/1361-6560/ac4442.
Texto completoHill, S. "Reduced gradient computation in prediction error identification". IEEE Transactions on Automatic Control 30, n.º 8 (agosto de 1985): 776–78. http://dx.doi.org/10.1109/tac.1985.1104062.
Texto completoCalugaru, Dan-Gabriel y Jean-Marie Crolet. "Gradient computation in a nonlinear inverse problem". Comptes Rendus Mathematique 336, n.º 8 (abril de 2003): 691–96. http://dx.doi.org/10.1016/s1631-073x(03)00130-4.
Texto completoBerlin, Konstantin, Nail A. Gumerov, David Fushman y Ramani Duraiswami. "HierarchicalO(N) computation of small-angle scattering profiles and their associated derivatives". Journal of Applied Crystallography 47, n.º 2 (28 de marzo de 2014): 755–61. http://dx.doi.org/10.1107/s1600576714004671.
Texto completoZhang, Jianfei y Lei Zhang. "Efficient CUDA Polynomial Preconditioned Conjugate Gradient Solver for Finite Element Computation of Elasticity Problems". Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/398438.
Texto completoYang, Jucheng, Xiaojing Wang, Shujie Han, Jie Wang, Dong Sun Park y Yuan Wang. "Improved Real-Time Facial Expression Recognition Based on a Novel Balanced and Symmetric Local Gradient Coding". Sensors 19, n.º 8 (22 de abril de 2019): 1899. http://dx.doi.org/10.3390/s19081899.
Texto completoSmistad, Erik y Frank Lindseth. "Multigrid gradient vector flow computation on the GPU". Journal of Real-Time Image Processing 12, n.º 3 (30 de octubre de 2014): 593–601. http://dx.doi.org/10.1007/s11554-014-0466-2.
Texto completoTesis sobre el tema "GRADIENT COMPUTATION"
Qiao, Lei Ph D. Massachusetts Institute of Technology. "Variational constitutive updates for strain gradient isotropic plasticity". Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/55079.
Texto completoCataloged from PDF version of thesis.
Includes bibliographical references (p. 93-96).
In the past decades, various strain gradient isotropic plasticity theories have been developed to describe the size-dependence plastic deformation mechanisms observed experimentally in micron-indentation, torsion, bending and thin-film bulge tests in metallic materials. Strain gradient plasticity theories also constitute a convenient device to introduce ellipticity in the differential equations governing plastic deformation in the presence of softening. The main challenge to the numerical formulations is that the effective plastic strain, a local internal variable in the classic isotropic plasticity theory, is now governed by the partial differential equation which includes spatial derivatives. Most of the current numerical formulations are based on Aifantis' one-parameter model with a Laplacian term [Aifantis and Muhlhaus, ijss, 28:845-857, 1991]. As indicated in the paper [Fleck and Hutchinson, jmps, 49:2245-2271, 2001], one parameter is not sufficient to match the experimental data. Therefore a robust and efficient computational framework that can deal with more parameters is still in need. In this thesis, a numerical formulation based on the framework of variational constitutive updates is presented to solve the initial boundary value problem in strain gradient isotropic plasticity. One advantage of this approach compared to the mixed methods is that it avoids the need to solve for both the displacement and the effective plastic strain fields simultaneously. Another advantage of this approach is, as has been amply established for many other material models, that the solution of the problem follows a minimum principle, thus providing a convenient basis for error estimation and adaptive remeshing.
(cont.) The advantages of the framework of variational constitutive updates have already been verified in a wide class of material models including visco-elasticity, visco-plasticity, crystal plasticity and soil, however this approach has not been implemented in the strain gradient plasticity models. In this thesis, a three-parameter strain gradient isotropic plasticity model is formulated within the variational framework, which is then taken as a basis for finite element discretization. The resulting model is implemented in a computer code and exercised on the benchmark problems to demonstrate the robustness and versatility of the proposed method.
by Lei Qiao.
S.M.
Damou, Merzak. "Measurement and computation of a turbulent jet in an axial pressure gradient". Thesis, University of Manchester, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305418.
Texto completoSitta, Alessandro. "Privacy-Preserving Distributed Optimization via Obfuscated Gradient Tracking". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Buscar texto completoAl-Mudhaf, Ali F. "A feed forward neural network approach for matrix computations". Thesis, Brunel University, 2001. http://bura.brunel.ac.uk/handle/2438/5010.
Texto completoSautter, Rubens Andreas. "Gradient Pattern Analysis: New methodological and computational features with applications". Instituto Nacional de Pesquisas Espaciais (INPE), 2018. http://urlib.net/sid.inpe.br/mtc-m21c/2018/05.07.12.09.
Texto completoIn this work it is presented the Gradient Pattern Analysis (GPA), a formalism that describes operators for analysis of spatially extended system, concerning its asymmetry. Aiming to work with large datasets, it is proposed improvements to the most popular version of GPA, with respect to the metric measurement and computational efficiency. We also review and explore the gradient moments, and propose two new operators. In order to validate the implementation of the operators G1 and G2, the following study cases are presented: (i) a dynamical study case in Coupled Map Lattices (CML), and (ii) a static case study in Galaxy Morphology. With respect to application (i), we analyze two system transitions: symmetry breaking and synchronization. Concerning the application (ii), it is presented a system of galaxy morphometrics named CyMorph, which has an important role on a project for studying the galaxies formation and evolution. The aim of CyMorph is to classify galaxies, between early-type and late-type using non-parametric morphometrics. G1 and G2 were integrated to CyMorph. We observe that G2 is the second-best morphometric in a system with 10 metrics.
Chauffour, Marie-Laure. "Shock-based waverider design with pressure gradient corrections and computational simulations". College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/1829.
Texto completoThesis research directed by: Dept. of Aerospace Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Fischer, Paul [Verfasser] y Paul [Akademischer Betreuer] Steinmann. "C1 Continuous Methods in Computational Gradient Elasticity / Paul Fischer. Betreuer: Paul Steinmann". Erlangen : Universitätsbibliothek der Universität Erlangen-Nürnberg, 2011. http://d-nb.info/1015783635/34.
Texto completoMiles, Alexander, William Duncan, Brian Klug y Colton Holmes. "Rapid Prototyped Terahertz-Domain Gradient Index Optics: Computational Design, Simulation, and Manufacture". International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595744.
Texto completoThere are a myriad of applications for terahertz radiation: security, military radar, product inspection, and telecommunications. These require manipulation of the radiation beyond simple transmission and detection, namely refraction: focusing, defocusing, and collimation. The current state of the art fabrication of terahertz lenses is an expensive and time consuming processes; involving high purity semiconductors and months of lead time. Our project focused on demonstrating that an inexpensive and quick process could reduce the production investment required by more than three orders of magnitude. This process is based on fabrication using a novel gradient index structure produced with polymer-jetting rapid-prototyping machine.
Thill, Serge. "A computational analysis of the gradient navigation strategies of the nematode Caenorhabditis elegans". Thesis, University of Leicester, 2008. http://hdl.handle.net/2381/4014.
Texto completoNorris, Michael K. "INCORPORATING HISTOGRAMS OF ORIENTED GRADIENTS INTO MONTE CARLO LOCALIZATION". DigitalCommons@CalPoly, 2016. https://digitalcommons.calpoly.edu/theses/1629.
Texto completoLibros sobre el tema "GRADIENT COMPUTATION"
Greenbaum, Anne. Predicting the behavior of finite precision Lanczos and conjugate gradient computations. New York: Courant Institute of Mathematical Sciences, New York University, 1991.
Buscar texto completoThe Lanczos and conjugate gradient algorithms: From theory to finite precision computations. Philadelphia: Society for Industrial and Applied Mathematics, 2006.
Buscar texto completoFu, Michael. Conditional Monte Carlo: Gradient Estimation and Optimization Applications. Boston, MA: Springer US, 1997.
Buscar texto completoG, Hinshaw y United States. National Aeronautics and Space Administration., eds. Three-point correlations in COBE DMR maps. [Washington, DC: National Aeronautics and Space Administration, 1995.
Buscar texto completo1929-, Chung T. J. y United States. National Aeronautics and Space Administration., eds. Flowfield-dependent mixed explicit-implicit (FDMEI) algorithm for computational fluid dynamics: Final report ... [Washington, DC: National Aeronautics and Space Administration, 1997.
Buscar texto completoP, Leonard B. y United States. National Aeronautics and Space Administration., eds. A modified mixing length turbulence model for zero and adverse pressure gradients. [Washington, DC]: National Aeronautics and Space Administration, 1994.
Buscar texto completoConley, J. M. A modified mixing length turbulence model for zero and adverse pressure gradients. [Washington, DC]: National Aeronautics and Space Administration, 1994.
Buscar texto completoD, Simon Horst, Tang Wei-Pai y Research Institute for Advanced Computer Science (U.S.), eds. Spectral ordering techniques for incomplete LU preconditioners for CG methods. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1995.
Buscar texto completoWang, Yan Ming. Downward continuation of the free-air gravity anomalies to the ellipsoid using the gradient solution, Poisson's integral and terrain correction-numerical comparison and the computations. Columbus, Ohio: Dept. of Geodetic Science and Surveying, Ohio State University, 1988.
Buscar texto completo1956-, Volakis John Leonidas y United States. National Aeronautics and Space Administration., eds. A finite element-boundary integral method for electromagnetic scattering. Ann Arbor, Mich: University of Michigan, Radiation Laboratory, Dept. of Electrical Engineering and Computer Science, 1992.
Buscar texto completoCapítulos de libros sobre el tema "GRADIENT COMPUTATION"
Moukalled, F., L. Mangani y M. Darwish. "Gradient Computation". En The Finite Volume Method in Computational Fluid Dynamics, 273–302. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16874-6_9.
Texto completoSabbagh, Harold A., R. Kim Murphy, Elias H. Sabbagh, Liming Zhou y Russell Wincheski. "A Bilinear Conjugate-Gradient Inversion Algorithm". En Scientific Computation, 3–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67956-9_1.
Texto completoRall, Louis B. "Gradient Computation by Matrix Multiplication". En Applied Mathematics and Parallel Computing, 233–40. Heidelberg: Physica-Verlag HD, 1996. http://dx.doi.org/10.1007/978-3-642-99789-1_16.
Texto completoJiang, Bo-nan. "The Element-by-Element Conjugate Gradient Method". En Scientific Computation, 385–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03740-9_15.
Texto completoŠolcová, Alena. "The Founders of the Conjugate Gradient Method". En Scientific Computation, 3–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18560-1_1.
Texto completoKřížek, Michal y Sergey Korotov. "Geometric Interpretations of Conjugate Gradient and Related Methods". En Scientific Computation, 25–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18560-1_3.
Texto completoVermolen, Fred, Kees Vuik y Guus Segal. "Deflation in Preconditioned Conjugate Gradient Methods for Finite Element Problems". En Scientific Computation, 103–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18560-1_7.
Texto completoRuppel, Philipp y Jianwei Zhang. "Efficent Gradient Propagation for Robot Control and Learning". En Cognitive Computation and Systems, 237–46. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2789-0_20.
Texto completoBücker*, H. Martin y Manfred Sauren. "Reducing Global Synchronization in the Biconjugate Gradient Method". En Parallel Numerical Computation with Applications, 63–76. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5205-5_5.
Texto completoSachs, Ekkehard W. y Matthias Schu. "Gradient Computation for Model Calibration with Pointwise Observations". En Control and Optimization with PDE Constraints, 117–36. Basel: Springer Basel, 2013. http://dx.doi.org/10.1007/978-3-0348-0631-2_7.
Texto completoActas de conferencias sobre el tema "GRADIENT COMPUTATION"
Křivánek, Jaroslav, Pascal Gautron, Kadi Bouatouch y Sumanta Pattanaik. "Improved radiance gradient computation". En ACM SIGGRAPH 2008 classes. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1401132.1401229.
Texto completoKřivánek, Jaroslav, Pascal Gautron, Kadi Bouatouch y Sumanta Pattanaik. "Improved radiance gradient computation". En the 21st spring conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1090122.1090148.
Texto completoSkilling, John, Paul M. Goggans y Chun-Yong Chan. "Conjugate Gradient for Bayesian Computation". En BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: The 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2009. http://dx.doi.org/10.1063/1.3275624.
Texto completoSon, Kyungrak y Aditya Ramamoorthy. "Coded matrix computation with gradient coding". En 2023 IEEE International Symposium on Information Theory (ISIT). IEEE, 2023. http://dx.doi.org/10.1109/isit54713.2023.10206996.
Texto completoBoyang Li, Yew-Soon Ong, Minh Nghia Le y Chi Keong Goh. "Memetic Gradient Search". En 2008 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2008. http://dx.doi.org/10.1109/cec.2008.4631187.
Texto completoLi, Vladimir, Hui Wang, Ilya Tsvankin, Esteban Diaz y Tariq Alkhalifah. "Gradient computation for VTI acoustic wavefield tomography". En SEG Technical Program Expanded Abstracts 2016. Society of Exploration Geophysicists, 2016. http://dx.doi.org/10.1190/segam2016-13967436.1.
Texto completoKera, Hiroshi. "Border Basis Computation with Gradient-Weighted Normalization". En ISSAC '22: International Symposium on Symbolic and Algebraic Computation. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3476446.3535476.
Texto completoMoraes, R., J. R. P. Rodrigues, H. Hajibeygi y J. D. Jansen. "Multiscale Gradient Computation for Subsurface Flow Models". En ECMOR XV - 15th European Conference on the Mathematics of Oil Recovery. Netherlands: EAGE Publications BV, 2016. http://dx.doi.org/10.3997/2214-4609.201601891.
Texto completoXu, Zhiqiang, Xin Cao y Xin Gao. "Convergence Analysis of Gradient Descent for Eigenvector Computation". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/407.
Texto completoIandola, Forrest N., Matthew W. Moskewicz y Kurt Keutzer. "libHOG: Energy-Efficient Histogram of Oriented Gradient Computation". En 2015 IEEE 18th International Conference on Intelligent Transportation Systems - (ITSC 2015). IEEE, 2015. http://dx.doi.org/10.1109/itsc.2015.205.
Texto completoInformes sobre el tema "GRADIENT COMPUTATION"
Burks, Thomas F., Victor Alchanatis y Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, octubre de 2009. http://dx.doi.org/10.32747/2009.7591739.bard.
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