Academic literature on the topic 'Parameterizations'
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Journal articles on the topic "Parameterizations"
Kuznetsova, Alexandra, Georgy Baydakov, Vladislav Papko, Alexander Kandaurov, Maxim Vdovin, Daniil Sergeev, and Yuliya Troitskaya. "Adjusting of Wind Input Source Term in WAVEWATCH III Model for the Middle-Sized Water Body on the Basis of the Field Experiment." Advances in Meteorology 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/8539127.
Full textJin, Han-Gyul, Hyunho Lee, and Jong-Jin Baik. "A New Parameterization of the Accretion of Cloud Water by Graupel and Its Evaluation through Cloud and Precipitation Simulations." Journal of the Atmospheric Sciences 76, no. 2 (January 21, 2019): 381–400. http://dx.doi.org/10.1175/jas-d-18-0245.1.
Full textLiu, Yangang, Peter H. Daum, R. McGraw, and R. Wood. "Parameterization of the Autoconversion Process. Part II: Generalization of Sundqvist-Type Parameterizations." Journal of the Atmospheric Sciences 63, no. 3 (March 1, 2006): 1103–9. http://dx.doi.org/10.1175/jas3675.1.
Full textBAJAJ, CHANDRAJIT L., and ANDREW V. ROYAPPA. "FINITE REPRESENTATIONS OF REAL PARAMETRIC CURVES AND SURFACES." International Journal of Computational Geometry & Applications 05, no. 03 (September 1995): 313–26. http://dx.doi.org/10.1142/s0218195995000180.
Full textWang, Wenping, Barry Joe, and Ronald Goldman. "Rational Quadratic Parameterizations of Quadrics." International Journal of Computational Geometry & Applications 07, no. 06 (December 1997): 599–619. http://dx.doi.org/10.1142/s0218195997000375.
Full textLee, Hyunho, and Jong-Jin Baik. "A Physically Based Autoconversion Parameterization." Journal of the Atmospheric Sciences 74, no. 5 (May 1, 2017): 1599–616. http://dx.doi.org/10.1175/jas-d-16-0207.1.
Full textXu, Shibo, and Alexey Stovas. "A new parameterization for acoustic orthorhombic media." GEOPHYSICS 82, no. 6 (November 1, 2017): C229—C240. http://dx.doi.org/10.1190/geo2017-0215.1.
Full textSong, Yong, Christopher K. Wikle, Christopher J. Anderson, and Steven A. Lack. "Bayesian Estimation of Stochastic Parameterizations in a Numerical Weather Forecasting Model." Monthly Weather Review 135, no. 12 (December 1, 2007): 4045–59. http://dx.doi.org/10.1175/2007mwr1928.1.
Full textPan, Wenyong, Kristopher A. Innanen, Yu Geng, and Junxiao Li. "Interparameter trade-off quantification for isotropic-elastic full-waveform inversion with various model parameterizations." GEOPHYSICS 84, no. 2 (March 1, 2019): R185—R206. http://dx.doi.org/10.1190/geo2017-0832.1.
Full textDe la Sen, M., and A. Ibeas. "Stability Results of a Class of Hybrid Systems under Switched Continuous-Time and Discrete-Time Control." Discrete Dynamics in Nature and Society 2009 (2009): 1–28. http://dx.doi.org/10.1155/2009/315713.
Full textDissertations / Theses on the topic "Parameterizations"
Brück, Heiner Matthias. "Evaluation of statistical cloud parameterizations." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-212714.
Full textZagoruyko, Sergey. "Weight parameterizations in deep neural networks." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1129/document.
Full textMultilayer neural networks were first proposed more than three decades ago, and various architectures and parameterizations were explored since. Recently, graphics processing units enabled very efficient neural network training, and allowed training much larger networks on larger datasets, dramatically improving performance on various supervised learning tasks. However, the generalization is still far from human level, and it is difficult to understand on what the decisions made are based. To improve on generalization and understanding we revisit the problems of weight parameterizations in deep neural networks. We identify the most important, to our mind, problems in modern architectures: network depth, parameter efficiency, and learning multiple tasks at the same time, and try to address them in this thesis. We start with one of the core problems of computer vision, patch matching, and propose to use convolutional neural networks of various architectures to solve it, instead of manual hand-crafting descriptors. Then, we address the task of object detection, where a network should simultaneously learn to both predict class of the object and the location. In both tasks we find that the number of parameters in the network is the major factor determining it's performance, and explore this phenomena in residual networks. Our findings show that their original motivation, training deeper networks for better representations, does not fully hold, and wider networks with less layers can be as effective as deeper with the same number of parameters. Overall, we present an extensive study on architectures and weight parameterizations, and ways of transferring knowledge between them
Moon, Todd K., and Krishna Kishor Noru. "WAVEFORM SIGNAL SHAPING USING WAVELET PARAMETERIZATIONS." International Foundation for Telemetering, 2001. http://hdl.handle.net/10150/607582.
Full textWe explore the idea of matching a scaling function - the basic building block of a wavelet function - to a desired spectrum. This would allow the scaling function to be used as the signal pulse for a digital communication system that is matched to the channel, avoiding problems such as energy loss or noise amplification due to spectral nulls. An unconstrained parameterization of the scaling function coefficients represents the scaling functions. This parameterization is adapted using gradient descent. Tests indicate that the adaptation is able to capture major features of a desired spectrum, including spectral nulls and major lobes.
Hales, Jonathan Reid. "Divisors of Modular Parameterizations of Elliptic Curves." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8472.
Full textKatz, Jeremy. "Evaluation of Surface Layer Parameterizations Using In-Situ Observations." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/2604.
Full textKim, Youngseob. "Air quality modeling : evaluation of chemical and meteorological parameterizations." Phd thesis, Université Paris-Est, 2011. http://pastel.archives-ouvertes.fr/pastel-00667777.
Full textQuaas, Johannes, and Philip Stier. "Satellite observations of convection and their implications for parameterizations." Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-203263.
Full textGriffin, Joshua Thomas. "Characterization of errors in various moisture roughness length parameterizations." Tallahassee, Florida : Florida State University, 2009. http://etd.lib.fsu.edu/theses/available/etd-11092009-112425/.
Full textAdvisor: Mark A. Bourassa, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (viewed on Apr. 8, 2010). Document formatted into pages; contains viii, 30 pages. Includes bibliographical references.
Wilkinson, Jonathan Mark. "Evaluating numerical model cloud parameterizations using radar and lidar simulation." Thesis, University of Reading, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440441.
Full textSolovev, Mikhail A. "Assessment of mesoscale eddy parameterizations for coarse resolution ocean models." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/58517.
Full textIncludes bibliographical references (p. 248-253).
Climate simulation with numerical oceanic models requires a proper parameterization scheme in order to represent the effects of unresolved mesoscale eddies. Even though a munber of schemes have been proposed and some have led to improvements in the simulation of the bulk climatological properties, the success of the parameterizations in representing the mesoscale eddies has not been investigated in detail. This thesis examines the role of eddies in a 105-years long basin scale eddy resolving simulation with the MIT General Circulation Model (GCM) forced by idealized wind stress and relaxation to prescribed meridional temperature; this thesis also evaluates the Fickian diffusive, the diabatic Green-Stone (GS) and the quasi-adiabatic Gent-McWilliams (GM) parameterizations in a diagnostic study and a series of coarse resolution experiments with the same model in the same configuration. The mesoscale eddies in the reference experiment provide a significant contribution to the thermal balance in limited areas of the domain associated with the upper 1000M of the boundary regions. Specifically designed diagnostic tests of the schemes show that the horizontal and vertical components of the parameterized flux are not simultaneously downgradient to the eddy heat flux. The transfer vectors are more closely aligned with the isopycnal surfaces for deeper layers, thus demonstrating the adiabatic nature of the eddy heat flux for deeper layers. The magnitude of the coefficients is estimated to be consistent with traditionally used values. However, the transfer of heat associated with timedependent motions is identified as a complicated process that cannot be fully explained with any of the local parameterization schemes considered. The eddy parameterization schemes are implemented in the coarse resolution configuration with the same model. A series of experiments exploring the schemes' parameter space demonstrate that Fickian diffusion has the least skill in the climatological simulations because it overestimates the temperature of the deep ocean and underestimates the total heat transport. The GS and GM schemes perform better in the simulation of the bulk climatological properties of the reference solution, although the GM scheme in particular produces an ocean that is consistently colder than the reference state. Comparison of the eddy heat flux divergence with the parameterized divergences for typical parameter values demonstrates that the success of the schemes in the climatological simulation is not related to the representation of the eddy heat flux but to the representation of the overall internal mixing processes.
by Mikhail A. Solovev.
Ph.D.
Books on the topic "Parameterizations"
1964-, Toro Tatiana, ed. Reifenberg parameterizations for sets with holes. Providence, R.I: American Mathematical Society, 2012.
Find full textCloud and precipitation microphysics: Principles and parameterizations. Cambridge: Cambridge University Press, 2009.
Find full textSolovev, Mikhail A. Assessment of mesoscale eddy parameterizations for coarse resolution ocean models. Cambridge, Mass: Massachusetts Institute of Technology, 1999.
Find full textWorkshop on Cloud Microphysics Parameterizations in Global Atmospheric Circulation Models (1995 Kananaskis, Alta.). Workshop on Cloud Microphysics Parameterizations in Global Atmospheric Circulation Models: Kananaskis, Alberta, Canada, 23-25 May 1995. [Geneva, Switzerland: Joint Planning Staff for WCRP c/o World Meteorological Organization, 1995.
Find full textChassignet, Eric P., and Jacques Verron, eds. Ocean Modeling and Parameterization. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5096-5.
Full textChassignet, Eric P. Ocean Modeling and Parameterization. Dordrecht: Springer Netherlands, 1998.
Find full textGanchev, Todor. Contemporary Methods for Speech Parameterization. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8447-0.
Full textContemporary methods for speech parameterization. New York: Springer, 2011.
Find full textTripathi, Ram K. Universal parameterization of absorption cross sections. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.
Find full textTripathi, Ratikanta. Universal parameterization of absorption cross sections. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.
Find full textBook chapters on the topic "Parameterizations"
Morawiec, Adam. "Parameterizations." In Orientations and Rotations, 19–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-09156-2_2.
Full textPoodiack, Robert D., and William E. Wood. "Parameterizations." In Springer Undergraduate Mathematics Series, 57–65. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13783-9_7.
Full textMisra, Neeldhara. "Alternate Parameterizations." In Encyclopedia of Algorithms, 64–67. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_786.
Full textMisra, Neeldhara. "Alternate Parameterizations." In Encyclopedia of Algorithms, 1–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27848-8_786-1.
Full textPoodiack, Robert D., and William E. Wood. "Analytic parameterizations." In Springer Undergraduate Mathematics Series, 221–33. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13783-9_20.
Full textThiry, Louis, Long Li, and Etienne Mémin. "Modified (Hyper-)Viscosity for Coarse-Resolution Ocean Models." In Mathematics of Planet Earth, 273–85. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18988-3_17.
Full textUliasz, Marek. "Subgrid-Scale Parameterizations." In Mesoscale Modeling of the Atmosphere, 13–19. Boston, MA: American Meteorological Society, 1994. http://dx.doi.org/10.1007/978-1-935704-12-6_2.
Full textBruhn, Henning, Morgan Chopin, Felix Joos, and Oliver Schaudt. "Structural Parameterizations for Boxicity." In Graph-Theoretic Concepts in Computer Science, 117–28. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12340-0_10.
Full textBailey, Barbara A., L. Mark Berliner, William Collins, Douglas W. Nychka, and Jeffrey T. Kiehl. "Neural Networks: Cloud Parameterizations." In Studies in the Atmospheric Sciences, 97–116. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-2112-8_7.
Full textLeclerc, Monique Y., and Thomas Foken. "Surface-Layer Properties and Parameterizations." In Footprints in Micrometeorology and Ecology, 21–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54545-0_2.
Full textConference papers on the topic "Parameterizations"
Bauchau, Olivier A., and Jou-Young Choi. "The Vectorial Parameterization of Motion." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/vib-48304.
Full textEno, D. R., G. A. Young, and T. L. Sham. "A Unified View of Engineering Creep Parameters." In ASME 2008 Pressure Vessels and Piping Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/pvp2008-61129.
Full textPraun, Emil, Wim Sweldens, and Peter Schröder. "Consistent mesh parameterizations." In the 28th annual conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/383259.383277.
Full textLina, Jean-Marc, and Michel Mayrand. "Parameterizations for complex Daubechies wavelets." In SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, edited by Harold H. Szu. SPIE, 1994. http://dx.doi.org/10.1117/12.170087.
Full textSantos, Daniel Rodrigues, André Ricardo Fioravanti, Antonio Alberto Souza Santos, and Denis José Schiozer. "Investigation of Well Control Parameterization with Reduced Number of Variables Under Reservoir Uncertainties." In SPE Europec featured at 82nd EAGE Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205207-ms.
Full textvan Hoeij, Mark. "Computing parameterizations of rational algebraic curves." In the international symposium. New York, New York, USA: ACM Press, 1994. http://dx.doi.org/10.1145/190347.190415.
Full textDong, Shen, and Michael Garland. "Iterative Methods for Improving Mesh Parameterizations." In IEEE International Conference on Shape Modeling and Applications 2007 (SMI '07). IEEE, 2007. http://dx.doi.org/10.1109/smi.2007.23.
Full textGrey, Zach, and Paul Constantine. "Active Subspaces of Airfoil Shape Parameterizations." In 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-0507.
Full textKENYON, RICHARD. "LIMIT SHAPES AND THEIR ANALYTIC PARAMETERIZATIONS." In International Congress of Mathematicians 2018. WORLD SCIENTIFIC, 2019. http://dx.doi.org/10.1142/9789813272880_0175.
Full textWang, Yuh-Shyang, Nikolai Matni, and John C. Doyle. "System Level Parameterizations, constraints and synthesis." In 2017 American Control Conference (ACC). IEEE, 2017. http://dx.doi.org/10.23919/acc.2017.7963133.
Full textReports on the topic "Parameterizations"
Jackson, Artie. Modified-Dewan Optical Turbulence Parameterizations. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada432901.
Full textTeixeira, Joao. A Framework to Evaluate Unified Parameterizations for Seasonal Prediction: An LES/SCM Parameterization Test-Bed. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada574019.
Full textTeixeira, Joao. A Framework to Evaluate Unified Parameterizations for Seasonal Prediction: An LES/SCM Parameterization Test-Bed. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada598084.
Full textHadzhilazova, Mariana, Ivaïlo Mladenov Ivaïlo Mladenov, and Peter Djondjorov. New Parameterizations of the Cassinian Ovals. GIQ, 2012. http://dx.doi.org/10.7546/giq-12-2011-237-243.
Full textGoodman, Louis. Development of Turbulent Biological Closure Parameterizations. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada505108.
Full textGoodman, Louis. Development of Turbulent Biological Closure Parameterizations. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada598069.
Full textLovell, Amy Elizabeth, Patrick Talou, Ionel Stetcu, Arvind Thanam Mohan, and Michael Chertkov. Constructing Global Fission Yield Parameterizations for CGMF. Office of Scientific and Technical Information (OSTI), December 2018. http://dx.doi.org/10.2172/1487353.
Full textGregg, Michael C., and Jack Miller. Flow Over Difficult Bathymetry: Processes and Parameterizations. Fort Belvoir, VA: Defense Technical Information Center, October 2003. http://dx.doi.org/10.21236/ada418045.
Full textBruce Albrecht. Boundary Layer Cloudiness Parameterizations Using ARM Observations. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/830034.
Full textGregg, Michael C., and Parker MacCready. Flow over Difficulty Bathymetry: Processes and Parameterizations. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada624733.
Full text