Littérature scientifique sur le sujet « High-order modeling »
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Articles de revues sur le sujet "High-order modeling"
Chang, Yuan Lung. « Inferring Markov Chain for Modeling Order Book Dynamics in High Frequency Environment ». International Journal of Machine Learning and Computing 5, no 3 (juin 2015) : 247–51. http://dx.doi.org/10.7763/ijmlc.2015.v5.515.
Texte intégralNewman, Christopher, Geoff Womeldorff, Luis Chacón et Dana A. Knoll. « High-Order/Low-Order Methods for Ocean Modeling ». Procedia Computer Science 51 (2015) : 2086–96. http://dx.doi.org/10.1016/j.procs.2015.05.477.
Texte intégralQing Yang, Qing Yang, Ning Li Qing Yang, Shiyan Hu Ning Li, Heyong Li Shiyan Hu et Jingwei Zhang Heyong Li. « Click-Through Rate Prediction Algorithm Based on Modeling of Implicit High-Order Feature Importance ». 網際網路技術學刊 23, no 5 (septembre 2022) : 1077–86. http://dx.doi.org/10.53106/160792642022092305016.
Texte intégralChen, Jing-Bo. « High-order time discretizations in seismic modeling ». GEOPHYSICS 72, no 5 (septembre 2007) : SM115—SM122. http://dx.doi.org/10.1190/1.2750424.
Texte intégralGavva, S. P. « Modeling of High-Order Overtone Molecular Vibrations ». Russian Physics Journal 48, no 3 (mars 2005) : 275–79. http://dx.doi.org/10.1007/s11182-005-0119-9.
Texte intégralTakeuchi, Ichiro, Kazuya Nakagawa et Koji Tsuda. « Machine Learning Algorithm for High-Order Interaction Modeling ». Journal of the Robotics Society of Japan 35, no 3 (2017) : 215–20. http://dx.doi.org/10.7210/jrsj.35.215.
Texte intégralDorf, M., M. Dorr, J. Hittinger, W. Lee et D. Ghosh. « High-order finite-volume modeling of drift waves ». Journal of Computational Physics 373 (novembre 2018) : 446–54. http://dx.doi.org/10.1016/j.jcp.2018.07.009.
Texte intégralHestholm, Stig. « Acoustic VTI modeling using high-order finite differences ». GEOPHYSICS 74, no 5 (septembre 2009) : T67—T73. http://dx.doi.org/10.1190/1.3157242.
Texte intégralTsai, Hsing-Chih. « Modeling concrete strength with high-order neural networks ». Neural Computing and Applications 27, no 8 (26 août 2015) : 2465–73. http://dx.doi.org/10.1007/s00521-015-2017-6.
Texte intégralHuang, Kai, Vadim Backman et Igal Szleifer. « Modeling High-Order Chromatin Structure in Single Cells ». Biophysical Journal 118, no 3 (février 2020) : 550a—551a. http://dx.doi.org/10.1016/j.bpj.2019.11.3010.
Texte intégralThèses sur le sujet "High-order modeling"
Charous, Aaron( Aaron Solomon). « High-order retractions for reduced-order modeling and uncertainty quantification ». Thesis, Massachusetts Institute of Technology, 2006. https://hdl.handle.net/1721.1/130904.
Texte intégralCataloged from the official PDF version of thesis.
Includes bibliographical references (pages 145-151).
Though computing power continues to grow quickly, our appetite to solve larger and larger problems grows just as fast. As a consequence, reduced-order modeling has become an essential technique in the computational scientist's toolbox. By reducing the dimensionality of a system, we are able to obtain approximate solutions to otherwise intractable problems. And because the methodology we develop is sufficiently general, we may agnostically apply it to a plethora of problems, whether the high dimensionality arises due to the sheer size of the computational domain, the fine resolution we require, or stochasticity of the dynamics. In this thesis, we develop time integration schemes, called retractions, to efficiently evolve the dynamics of a system's low-rank approximation. Through the study of differential geometry, we are able to analyze the error incurred at each time step. A novel, explicit, computationally inexpensive set of algorithms, which we call perturbative retractions, are proposed that converge to an ideal retraction that projects exactly to the manifold of fixed-rank matrices. Furthermore, each perturbative retraction itself exhibits high-order convergence to the best low-rank approximation of the full-rank solution. We show that these high-order retractions significantly reduce the numerical error incurred over time when compared to a naive Euler forward retraction. Through test cases, we demonstrate their efficacy in the cases of matrix addition, real-time data compression, and deterministic and stochastic differential equations.
by Aaron Charous.
S.M.
S.M. Massachusetts Institute of Technology, Center for Computational Science & Engineering
Charous, Aaron (Aaron Solomon). « High-order retractions for reduced-order modeling and uncertainty quantification ». Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130904.
Texte intégralCataloged from the official PDF version of thesis.
Includes bibliographical references (pages 145-151).
Though computing power continues to grow quickly, our appetite to solve larger and larger problems grows just as fast. As a consequence, reduced-order modeling has become an essential technique in the computational scientist's toolbox. By reducing the dimensionality of a system, we are able to obtain approximate solutions to otherwise intractable problems. And because the methodology we develop is sufficiently general, we may agnostically apply it to a plethora of problems, whether the high dimensionality arises due to the sheer size of the computational domain, the fine resolution we require, or stochasticity of the dynamics. In this thesis, we develop time integration schemes, called retractions, to efficiently evolve the dynamics of a system's low-rank approximation. Through the study of differential geometry, we are able to analyze the error incurred at each time step. A novel, explicit, computationally inexpensive set of algorithms, which we call perturbative retractions, are proposed that converge to an ideal retraction that projects exactly to the manifold of fixed-rank matrices. Furthermore, each perturbative retraction itself exhibits high-order convergence to the best low-rank approximation of the full-rank solution. We show that these high-order retractions significantly reduce the numerical error incurred over time when compared to a naive Euler forward retraction. Through test cases, we demonstrate their efficacy in the cases of matrix addition, real-time data compression, and deterministic and stochastic differential equations.
by Aaron Charous.
S.M.
S.M. Massachusetts Institute of Technology, Center for Computational Science & Engineering
Velechovsky, Jan. « High-order numerical methods for laser plasma modeling ». Thesis, Bordeaux, 2015. http://www.theses.fr/2015BORD0098/document.
Texte intégralThis thesis presents the overview and the original contributions to a high–orderArbitrary Lagrangian–Eulerian (ALE) method applicable for the laser–generated plasma modeling withthe focus to a remapping step of the ALE method. The remap is the conservative interpolation of theconservative quantities from a low–quality Lagrangian grid onto a better, smoothed one. To avoidnon–physical numerical oscillations, the high–order numerical fluxes of the reconstruction arecombined with the low–order (first–order) numerical fluxes produced by a standard donor remappingmethod. The proposed method for a cell–centered discretization preserves bounds for the density,velocity and specific internal energy by its construction. Particular symmetry–preserving aspects of themethod are applied for a staggered momentum remap. The application part of the thesis is devoted tothe laser radiation absorption modeling in plasmas and microstructures materials with the particularinterest in the laser absorption in low–density foams. The absorption is modeled on two spatial scalessimultaneously. This two–scale laser absorption model is implemented in the hydrodynamic codePALE. The numerical simulations of the velocity of laser penetration in a low–density foam are in agood agreement with the experimental data
Heidkamp, Holger [Verfasser]. « Modeling Localization and Failure with High-Order Finite Elements / Holger Heidkamp ». Aachen : Shaker, 2008. http://d-nb.info/1164341642/34.
Texte intégralBeisiegel, Nicole [Verfasser], et Jörn [Akademischer Betreuer] Behrens. « High-order Adaptive Discontinuous Galerkin Inundation Modeling / Nicole Beisiegel. Betreuer : Jörn Behrens ». Hamburg : Staats- und Universitätsbibliothek Hamburg, 2014. http://d-nb.info/1060484749/34.
Texte intégralBeisiegel, Nicole Verfasser], et Jörn [Akademischer Betreuer] [Behrens. « High-order Adaptive Discontinuous Galerkin Inundation Modeling / Nicole Beisiegel. Betreuer : Jörn Behrens ». Hamburg : Staats- und Universitätsbibliothek Hamburg, 2014. http://nbn-resolving.de/urn:nbn:de:gbv:18-70360.
Texte intégralTong, Oisin. « Development of a Three-Dimensional High-Order Strand-Grids Approach ». DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/4711.
Texte intégralYe, Fei. « Developing Efficient High-Order Transport Schemes for Cross-Scale Coupled Estuary-Ocean Modeling ». W&M ScholarWorks, 2017. https://scholarworks.wm.edu/etd/1516639591.
Texte intégralCollins, Justin A. Valentine Jerry. « Higher-order thinking in the high-stakes accountability era linking student engagement and test performance / ». Diss., Columbia, Mo. : University of Missouri-Columbia, 2009. http://hdl.handle.net/10355/6769.
Texte intégralPowers, Sean W. « Analysis of Stresses in Metal Sheathed Thermocouples in High-Temperature, Hypersonic Flows ». Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98000.
Texte intégralM.S.
Thermocouples are a device for measuring temperature, consisting of two wires of different metals connected at two different points. This configuration produces a temperature-dependent voltage as a result of the thermoelectric effect. Preexisting curves are used to relate the voltage to temperature. Thermocouples are extensively used in high-temperature high-stress environments such as in rockets, jet engines, or any high-corrosive environment. Accurately predicting the stresses within the sheath of a metal-clad thermocouple in extreme conditions is required for many research areas including hypersonic aerodynamics and various propulsion applications. Even for these extremely well-developed and widely used sensors, the accurate prediction of stresses within the metal sheath remains a topic of great concern for ensuring the sensor’s survivability in these extreme conditions. Current engineering practice is to use high-fidelity numerical simulations (Finite Element Analysis) to predict the stresses within the sheath. Perhaps the biggest drawback to this approach is the time it takes to model, mesh, and set-up these simulations. Comparative studies between different designs using numerical simulations are almost impossible due to the time requirement. This Thesis will present an analytically derived quasi-3D solution to find the stresses within the sheath. These equations were implemented into a low-order model that can handle varying temperature, geometry, and material inputs. This model was validated against both high-fidelity numerical simulations (ANSYS Mechanical) and a simplified experiment. The predictions using this newly developed structural low-order model are in excellent agreement with the numerically simulated results and experimental results.
Livres sur le sujet "High-order modeling"
Thomas, Gregory Robert. A combined high-order spectral and boundary integral equation method for modelling wave interactions with submerged bodies. Springfield, Va : Available from National Technical Information Service, 1996.
Trouver le texte intégralHabib, Ammari, Capdeboscq Yves 1971- et Kang Hyeonbae, dir. Multi-scale and high-contrast PDE : From modelling, to mathematical analysis, to inversion : Conference on Multi-scale and High-contrast PDE:from Modelling, to Mathematical Analysis, to Inversion, June 28-July 1, 2011, University of Oxford, United Kingdom. Providence, R.I : American Mathematical Society, 2010.
Trouver le texte intégralReduced Order Modeling For High Speed Flows with Moving Shocks. Storming Media, 2001.
Trouver le texte intégralAbgrall, Rémi, Pietro Marco Congedo, Cécile Dobrzynski, Héloïse Beaugendre et Vincent Perrier. High Order Nonlinear Numerical Schemes for Evolutionary PDEs : Proceedings of the European Workshop HONOM 2013, Bordeaux, France, March 18-22 2013. Springer London, Limited, 2014.
Trouver le texte intégralHigh Order Nonlinear Numerical Schemes for Evolutionary PDEs : Proceedings of the European Workshop HONOM 2013, Bordeaux, France, March 18-22 2013. Springer, 2014.
Trouver le texte intégralMauranen, Anna. Second-Order Language Contact. Sous la direction de Markku Filppula, Juhani Klemola et Devyani Sharma. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199777716.013.010.
Texte intégralShaikh, Mohd Faraz. Machine Learning in Detecting Auditory Sequences in Magnetoencephalography Data : Research Project in Computational Modelling and Simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.25368/2022.411.
Texte intégralChapitres de livres sur le sujet "High-order modeling"
Givoli, Dan. « Non-Reflecting Boundaries : High-Order Treatment ». Dans A Celebration of Mathematical Modeling, 53–72. Dordrecht : Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-94-017-0427-4_4.
Texte intégralBottacchi, Stefano. « Theory and Modeling of Complex Optical Modulations ». Dans Handbook of High-Order Optical Modulations, 331–573. New York, NY : Springer New York, 2021. http://dx.doi.org/10.1007/978-1-0716-1195-1_4.
Texte intégralBottacchi, Stefano. « Statistical Modeling of PAM Signals and Power Spectra ». Dans Handbook of High-Order Optical Modulations, 203–329. New York, NY : Springer New York, 2021. http://dx.doi.org/10.1007/978-1-0716-1195-1_3.
Texte intégralAnastassiou, George A., et Oktay Duman. « High Order Statistical Fuzzy Korovkin-Type Approximation Theory ». Dans Towards Intelligent Modeling : Statistical Approximation Theory, 199–206. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19826-7_16.
Texte intégralGuillamet, David, Baback Moghaddam et Jordi Vitrià. « Modeling High-Order Dependencies in Local Appearance Models ». Dans Pattern Recognition and Image Analysis, 308–16. Berlin, Heidelberg : Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_36.
Texte intégralKim, Byung-soo, Min Sun, Pushmeet Kohli et Silvio Savarese. « Relating Things and Stuff by High-Order Potential Modeling ». Dans Computer Vision – ECCV 2012. Workshops and Demonstrations, 293–304. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33885-4_30.
Texte intégralSt.-Cyr, Amik, et Stephen J. Thomas. « High-Order Finite Element Methods for Parallel Atmospheric Modeling ». Dans Lecture Notes in Computer Science, 256–62. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11428831_32.
Texte intégralCheng, Yi-Chung, et Sheng-Tun Li. « A Best-Match Forecasting Model for High-Order Fuzzy Time Series ». Dans Time Series Analysis, Modeling and Applications, 331–45. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33439-9_15.
Texte intégralArmenta, Roberto B., et Costas D. Sarris. « Boundary Modeling and High-Order Convergence in Finite-Difference Methods ». Dans Computational Electromagnetics—Retrospective and Outlook, 225–43. Singapore : Springer Singapore, 2014. http://dx.doi.org/10.1007/978-981-287-095-7_9.
Texte intégralWang, Pan. « Finite-Time Stability Analysis of Fractional-Order High-Order Hopfield Neural Networks with Delays ». Dans Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems, 121–30. Singapore : Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2672-0_13.
Texte intégralActes de conférences sur le sujet "High-order modeling"
Zahr, M. « High-Order Implicit Shock Tracking ». Dans 10th International Conference on Adaptative Modeling and Simulation. CIMNE, 2021. http://dx.doi.org/10.23967/admos.2021.047.
Texte intégralKazakov, Vasily I., Oleg D. Moskaletz et Mikhail A. Vaganov. « High-order transmissive diffraction grating for high-resolution spectral systems ». Dans Modeling Aspects in Optical Metrology VII, sous la direction de Bernd Bodermann, Karsten Frenner et Richard M. Silver. SPIE, 2019. http://dx.doi.org/10.1117/12.2526004.
Texte intégralJohnson, Olin. « High order finite-difference modeling on supercomputers ». Dans 1985 SEG Technical Program Expanded Abstracts. SEG, 1985. http://dx.doi.org/10.1190/1.1892865.
Texte intégralWang, Qi, Li-xin Wang et Qiang Shen. « Modeling strategy of high order ARMA model ». Dans 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC). IEEE, 2016. http://dx.doi.org/10.1109/cgncc.2016.7829097.
Texte intégralShumlak, U., J. B. Coughlin, D. W. Crews, I. A. M. Datta, A. Ho, A. R. Johansen, E. T. Meier, Y. Takagaki et W. R. Thomas. « High-Order Finite Element Method for High-Fidelity Plasma Modeling ». Dans 2020 IEEE International Conference on Plasma Science (ICOPS). IEEE, 2020. http://dx.doi.org/10.1109/icops37625.2020.9717941.
Texte intégralCrabill, Jacob A., Jayanarayanan Sitaraman et Antony Jameson. « A High-Order Overset Method on Moving and Deforming Grids ». Dans AIAA Modeling and Simulation Technologies Conference. Reston, Virginia : American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-3225.
Texte intégralSai, Ryuichi, John Mellor-Crummey, Xiaozhu Meng, Mauricio Araya-Polo et Jie Meng. « Accelerating High-Order Stencils on GPUs ». Dans 2020 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS). IEEE, 2020. http://dx.doi.org/10.1109/pmbs51919.2020.00014.
Texte intégralShumlak, U., R. Lilly, S. Miller, N. Reddell et E. Sousa. « High-order finite element method for plasma modeling ». Dans 2013 IEEE 40th International Conference on Plasma Sciences (ICOPS). IEEE, 2013. http://dx.doi.org/10.1109/plasma.2013.6634927.
Texte intégralFriedrich, L., M. Curti, B. Gysen, J. Jansen et E. Lomonova. « High-order methods applied to electrical machine modeling. » Dans 2018 IEEE International Magnetic Conference (INTERMAG). IEEE, 2018. http://dx.doi.org/10.1109/intmag.2018.8508189.
Texte intégralPoggie, Jonathan. « High-Order Numerical Methods for Electrical Discharge Modeling ». Dans 41st Plasmadynamics and Lasers Conference. Reston, Virigina : American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-4632.
Texte intégralRapports d'organisations sur le sujet "High-order modeling"
Parish, Eric. Multiscale modeling high-order methods and data-driven modeling. Office of Scientific and Technical Information (OSTI), octobre 2020. http://dx.doi.org/10.2172/1673827.
Texte intégralMavriplis, Dimitri J. High-Order Modeling of Applied Multi-Physics Phenomena. Fort Belvoir, VA : Defense Technical Information Center, septembre 2009. http://dx.doi.org/10.21236/ada513855.
Texte intégralBeattie, Christopher A., Jeffrey T. Borggaard, Serkan Gugercin et Traian Iliescu. High Performance Parallel Algorithms for Improved Reduced-Order Modeling. Fort Belvoir, VA : Defense Technical Information Center, mai 2008. http://dx.doi.org/10.21236/ada483934.
Texte intégralCarlberg, Kevin, Micah Howard et Brian Freno. Rapid high-fidelity aerothermal responses with quantified uncertainties via reduced-order modeling. Office of Scientific and Technical Information (OSTI), août 2018. http://dx.doi.org/10.2172/1464878.
Texte intégralKrispin, Jacob, Mark Potts, Brady Brown, Ralph Ferguson et James Collins. High-Order Godunov Schemes for Multiphase Gas-Particulate Flowfield Modeling and Simulation. Fort Belvoir, VA : Defense Technical Information Center, septembre 2000. http://dx.doi.org/10.21236/ada385335.
Texte intégralPovitsky, A., et H. Gopalan. Modeling of Flow about Pitching and Plunging Airfoil Using High-Order Schemes. Fort Belvoir, VA : Defense Technical Information Center, mars 2008. http://dx.doi.org/10.21236/ada478589.
Texte intégralZhang, Guannan, Clayton G. Webster et Max D. Gunzburger. An adaptive sparse-grid high-order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling. Office of Scientific and Technical Information (OSTI), septembre 2012. http://dx.doi.org/10.2172/1055118.
Texte intégralHeitman, Joshua L., Alon Ben-Gal, Thomas J. Sauer, Nurit Agam et John Havlin. Separating Components of Evapotranspiration to Improve Efficiency in Vineyard Water Management. United States Department of Agriculture, mars 2014. http://dx.doi.org/10.32747/2014.7594386.bard.
Texte intégralSemerikov, Serhiy, Hanna Kucherova, Vita Los et Dmytro Ocheretin. Neural Network Analytics and Forecasting the Country's Business Climate in Conditions of the Coronavirus Disease (COVID-19). CEUR Workshop Proceedings, avril 2021. http://dx.doi.org/10.31812//123456789/4364.
Texte intégralRusso, David, Daniel M. Tartakovsky et Shlomo P. Neuman. Development of Predictive Tools for Contaminant Transport through Variably-Saturated Heterogeneous Composite Porous Formations. United States Department of Agriculture, décembre 2012. http://dx.doi.org/10.32747/2012.7592658.bard.
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