Academic literature on the topic 'Solution adaptive'

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Journal articles on the topic "Solution adaptive"

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LIN, Ying, Liang GE, and Run-liang DOU. "Adaptive error-check solution." Journal of Computer Applications 29, no. 1 (May 31, 2009): 5–8. http://dx.doi.org/10.3724/sp.j.1087.2009.00005.

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Zielinnski, K., T. Szydlo, R. Szymacha, J. Kosinski, J. Kosinska, and M. Jarzab. "Adaptive SOA Solution Stack." IEEE Transactions on Services Computing 5, no. 2 (April 2012): 149–63. http://dx.doi.org/10.1109/tsc.2011.8.

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Khodier, Ahmed M. M., and Adel Y. Hassan. "One-dimensional adaptive grid generation." International Journal of Mathematics and Mathematical Sciences 20, no. 3 (1997): 577–84. http://dx.doi.org/10.1155/s0161171297000781.

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In this work, we give an adaptive grid generation method which allows a single point to be added in the regions of large variation. This method uses a quadrature rule as a weight function. Our weight function measures the variation of the solution function on each subinterval of the solution domain. The method is applied to obtain the numerical solutions of some differential equations. A comparison of the numerical solution obtained by this method and other methods is given.
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Al-Afandi, Jalal, and András Horváth. "Adaptive Gene Level Mutation." Algorithms 14, no. 1 (January 9, 2021): 16. http://dx.doi.org/10.3390/a14010016.

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Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.
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Al-afandi, Jalal, and Horváth András. "Adaptive Gene Level Mutation." Algorithms 14, no. 1 (January 9, 2021): 16. http://dx.doi.org/10.3390/a14010016.

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Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.
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Gu, Yaguang, and Guanghui Hu. "A Third Order Adaptive ADER Scheme for One Dimensional Conservation Laws." Communications in Computational Physics 22, no. 3 (July 6, 2017): 829–51. http://dx.doi.org/10.4208/cicp.oa-2016-0088.

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AbstractWe introduce a third order adaptive mesh method to arbitrary high order Godunov approach. Our adaptive mesh method consists of two parts, i.e., mesh-redistribution algorithm and solution algorithm. The mesh-redistribution algorithm is derived based on variational approach, while a new solution algorithm is developed to preserve high order numerical accuracy well. The feature of proposed Adaptive ADER scheme includes that 1). all simulations in this paper are stable for large CFL number, 2). third order convergence of the numerical solutions is successfully observed with adaptive mesh method, and 3). high resolution and non-oscillatory numerical solutions are obtained successfully when there are shocks in the solution. A variety of numerical examples show the feature well.
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Hoppe, Ronald H. W., and Barbara Wohlmuth. "Efficient numerical solution of mixed finite element discretizations by adaptive multilevel methods." Applications of Mathematics 40, no. 3 (1995): 227–48. http://dx.doi.org/10.21136/am.1995.134292.

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Wang, Y., L. Fan, D. He, and R. Tafazolli. "Solution to weight-adaptive fair queuing." Electronics Letters 44, no. 5 (2008): 385. http://dx.doi.org/10.1049/el:20083333.

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Crawford, Paul M., and Katsuhiko Yokoyama. "An Adaptive Solution in Laboratory Automation." JAPAN TAPPI JOURNAL 62, no. 8 (2008): 994–98. http://dx.doi.org/10.2524/jtappij.62.994.

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Shen, Cherng-Yeu, Helen L. Reed, and Thomas A. Foley. "Shepard's Interpolation for Solution-Adaptive Methods." Journal of Computational Physics 106, no. 1 (May 1993): 52–61. http://dx.doi.org/10.1006/jcph.1993.1090.

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Dissertations / Theses on the topic "Solution adaptive"

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Kühnlein, Christian. "Solution-adaptive moving mesh solver for geophysical flows." Diss., lmu, 2011. http://nbn-resolving.de/urn:nbn:de:bvb:19-135874.

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Ghoneim, Youssef Ahmed. "Indirect adaptive control using the linear quadratic solution." Thesis, McGill University, 1985. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=72028.

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This thesis studies the indirect adaptive control for discrete linear time invariant systems. The adaptive control strategy is based on the linear quadratic regulator that places the closed loop poles such that an infinite stage quadratic cost function is minimized. The plant parameters are identified recursively using a projection algorithm.
First, we study the effect of the model over-parametrization. For this purpose, we introduce an algorithm to generate the controller parameters recursively. This asymptotic reformulation is shown to overcome situations in which the pole-zero cancellation is a limit point of the identification algorithm. We also show that the algorithm will generate a unique control sequence that converges asymptotically to the solution of the Diophantine (pole assignment) equation.
Next, we study the stability of the proposed adaptive scheme in both deterministic and stochastic cases. We show that the global stability of the resulting adaptive scheme is obtained with no implicit assumptions about parameter convergence or the nature of the external input. Then the global convergence of the adaptive algorithm is obtained if the external input is "persistently exciting". By convergence we mean that the adaptive control will converge to the optimal control of the system.
The performance of the adaptive algorithm in the presence of deterministic disturbances is also considered, where we show that the adaptive controller performs relatively well if the model order is high enough to include a description of the disturbances.
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Jackson, Nicolas Alain. "Adaptive finite element solution of option pricing problems." Thesis, University of Oxford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.393612.

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Robertson, Mairi Laidlaw. "The adaptive numerical solution of phase change problems." Thesis, University of Strathclyde, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366834.

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Várnai, Csilla. "Adaptive QM/MM simulations for reactions in solution." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609933.

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Kasmai, Naser Talon Shamsi. "Solution adaptive meshing strategies for flows with vortices." Master's thesis, Mississippi State : Mississippi State University, 2008. http://library.msstate.edu/etd/show.asp?etd=etd-07082008-134106.

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Juarez-Romero, David. "The adaptive solution of evolutive and equilibrium distributed systems." Thesis, Imperial College London, 1988. http://hdl.handle.net/10044/1/7461.

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Walsh, Paul Charles. "Adaptive solution of viscous aerodynamic flows using unstructured grids." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0014/NQ35361.pdf.

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Senguttuvan, Vinoad. "Solution adaptive isotropic and anisotropic mesh refinement using general elements." Master's thesis, Mississippi State : Mississippi State University, 2005. http://library.msstate.edu/etd/show.asp?etd=etd-04062005-104308.

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Saeed, Usman. "Adaptive numerical techniques for the solution of electromagnetic integral equations." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41173.

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Various error estimation and adaptive refinement techniques for the solution of electromagnetic integral equations were developed. Residual based error estimators and h-refinement implementations were done for the Method of Moments (MoM) solution of electromagnetic integral equations for a number of different problems. Due to high computational cost associated with the MoM, a cheaper solution technique known as the Locally-Corrected Nyström (LCN) method was explored. Several explicit and implicit techniques for error estimation in the LCN solution of electromagnetic integral equations were proposed and implemented for different geometries to successfully identify high-error regions. A simple p-refinement algorithm was developed and implemented for a number of prototype problems using the proposed estimators. Numerical error was found to significantly reduce in the high-error regions after the refinement. A simple computational cost analysis was also presented for the proposed error estimation schemes. Various cost-accuracy trade-offs and problem-specific limitations of different techniques for error estimation were discussed. Finally, a very important problem of slope-mismatch in the global error rates of the solution and the residual was identified. A few methods to compensate for that mismatch using scale factors based on matrix norms were developed.
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Books on the topic "Solution adaptive"

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Lang, Jens. Adaptive Multilevel Solution of Nonlinear Parabolic PDE Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04484-1.

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Walsh, Paul Charles. Adaptive solution of viscous aerodynamic flows using unstructured grids. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1998.

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Shapiro, Richard A. Adaptive finite element solution algorithm for the Euler equations. Braunschweig: Vieweg, 1991.

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service), SpringerLink (Online, ed. Numerical solution of Variational Inequalities by Adaptive Finite Elements. Wiesbaden: Vieweg+Teubner Verlag / GWV Fachverlage GmbH, Wiesbaden, 2008.

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Shapiro, Richard A., ed. Adaptive Finite Element Solution Algorithm for the Euler Equations. Wiesbaden: Vieweg+Teubner Verlag, 1991. http://dx.doi.org/10.1007/978-3-322-87879-3.

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Coirier, William J. Solution-adaptive Cartesian cell approach for viscous and inviscid flows. Washington, DC: American Institute of Aeronautics and Astronautics, 1996.

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Stals, Linda. The solution of radiation transport equations with adaptive finite elements. Hampton, Va: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 2001.

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Coirier, William J. Solution-adaptive Cartesian cell approach for viscous and inviscid flows. Washington, DC: American Institute of Aeronautics and Astronautics, 1996.

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Coirier, William J. Solution-adaptive Cartesian cell approach for viscous and inviscid flows. Washington, DC: American Institute of Aeronautics and Astronautics, 1996.

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Predovic, D. Tom. Multigrid solution of the Euler equations using unstructured adaptive meshes. [Downsview, Ont.]: University of Toronto, Dept. of Aerospace Science and Engineering, 1994.

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Book chapters on the topic "Solution adaptive"

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Rohrs, Jeffrey K. "The Enterprise Solution on Data." In Adaptive Marketing, 161–64. New York: Palgrave Macmillan US, 2015. http://dx.doi.org/10.1057/9781137462930_12.

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Schlickum, Caspar. "The Programmatic Solution on Data." In Adaptive Marketing, 108–10. New York: Palgrave Macmillan US, 2015. http://dx.doi.org/10.1057/9781137462930_8.

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Brockmann, T. H. "Solution Variants." In Theory of Adaptive Fiber Composites, 155–68. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2435-0_9.

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Shapiro, Richard A. "Solution Algorithm." In Adaptive Finite Element Solution Algorithm for the Euler Equations, 22–41. Wiesbaden: Vieweg+Teubner Verlag, 1991. http://dx.doi.org/10.1007/978-3-322-87879-3_4.

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Klopfer, G. H. "Hyperbolic Solution Adaptive Mesh Generation." In Advances in Fluid Dynamics, 91–102. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3684-9_8.

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Lin, Ying, Liang Ge, and Yueheng Sun. "AN ADAPTIVE ERROR-CHECK SOLUTION." In IFIP Advances in Information and Communication Technology, 1125–34. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0211-5_39.

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Ekstrand, Pär, and Laszlo Fuchs. "Adaptive Solution of Transonic Flows." In Computational Mechanics ’95, 1071–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79654-8_175.

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Weber, Christian. "STUDIO: A Solution on Adaptive Testing." In Corporate Knowledge Discovery and Organizational Learning, 131–53. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28917-5_6.

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Lenzen, Frank, and Johannes Berger. "Solution-Driven Adaptive Total Variation Regularization." In Lecture Notes in Computer Science, 203–15. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18461-6_17.

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Roubíček, Tomáš, and Martin Kružík. "Adaptive Approximation Algorithm for Relaxed Optimization Problems." In Fast Solution of Discretized Optimization Problems, 242–54. Basel: Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8233-0_18.

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Conference papers on the topic "Solution adaptive"

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IMLAY, SCOTT. "A solution adaptive grid/Navier-Stokes solution procedure." In 23rd Joint Propulsion Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1987. http://dx.doi.org/10.2514/6.1987-2180.

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Freeman, Matthew, Jessica Lu, Jacques R. Delorme, Jim E. Lyke, Emily Ramey, Grace Jung, Sylvain Cetre, et al. "An optical distortion solution for the Keck1 OSIRIS Imager." In Adaptive Optics Systems VIII, edited by Dirk Schmidt, Laura Schreiber, and Elise Vernet. SPIE, 2022. http://dx.doi.org/10.1117/12.2630712.

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Wang, Yingjie, Hongyan Deng, Jing Cui, Zhuoyuan Yu, and Ling Liu. "An Adaptive Personalized GIS Solution." In 2009 WRI Global Congress on Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/gcis.2009.59.

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Valenta, Václav, Václav Šátek, Jiří Kunovský, and Patricia Humenná. "Adaptive solution of Laplace equation." In 11TH INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2013: ICNAAM 2013. AIP, 2013. http://dx.doi.org/10.1063/1.4825996.

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Valenta, Václav, Gabriela Necasová, Jirí Kunovský, Václav Šátek, and Filip Kocina. "Adaptive Solution of the Wave Equation." In 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005539401540162.

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Spradley, Lawrence, Lajpat Utreja, Rainald Loehner, and J. Alexander. "Solution-adaptive transient crystal growth model." In Space Programs and Technologies Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1995. http://dx.doi.org/10.2514/6.1995-3578.

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Eigner, Gyorgy, Jozsef K. Tar, and Levente Kovacs. "Adaptive control solution for T1DM control." In 2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2015. http://dx.doi.org/10.1109/saci.2015.7208202.

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Tchon, Ko-Foa, Francois Guibault, Julien Dompierre, Paul Labbe, and Ricardo Camarero. "Solution Adaptive Refinement of Multiblock Decompositions." In 16th AIAA Computational Fluid Dynamics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2003. http://dx.doi.org/10.2514/6.2003-3821.

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Dogariu, Laura Maria, Silviu Ciochina, Constantin Paleologu, Jacob Benesty, and Pablo Piantanida. "An adaptive solution for nonlinear system identification." In 2017 International Symposium on Signals, Circuits and Systems (ISSCS). IEEE, 2017. http://dx.doi.org/10.1109/isscs.2017.8034918.

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Wang, Hongbing, and Xiaohui Guo. "An Adaptive Solution for Web Service Composition." In 2010 IEEE Congress on Services (SERVICES). IEEE, 2010. http://dx.doi.org/10.1109/services.2010.20.

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Reports on the topic "Solution adaptive"

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Dr. Cassiano R. E de Oliveira. Hierarchical Adaptive Solution of Radiation Transport Problems on Unstructured Grids. Office of Scientific and Technical Information (OSTI), June 2008. http://dx.doi.org/10.2172/934789.

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Henshaw, W. D., K. I. Pao, and J. S. Saltzman. Solution adaptive methods for low-speed and all-speed flows. Office of Scientific and Technical Information (OSTI), November 1998. http://dx.doi.org/10.2172/677160.

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Henshaw, W. D., K. I. Pao, and J. S. Saltzman. Solution adaptive methods for low-speed and all-speed flows. Office of Scientific and Technical Information (OSTI), January 1998. http://dx.doi.org/10.2172/758825.

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Orkwis, Paul D. A Study of Asymmetric Vortex Shedding Behind Missiles at High Angle of Attack Using Dynamic Solution Adaptive Meshes. Fort Belvoir, VA: Defense Technical Information Center, October 1995. http://dx.doi.org/10.21236/ada304583.

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Menon, Suresh, Tim Gallagher, and Balaji Muralidharan. Hybrid Solution-Adaptive Unstructured Cartesian Method for Large-Eddy Simulation of Detonation in Multi-Phase Turbulent Reactive Mixtures. Fort Belvoir, VA: Defense Technical Information Center, March 2012. http://dx.doi.org/10.21236/ada567123.

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Zhang, Cheng, and Yue Yang. Impact of adaptive design on reducing the duration of clinical trials in rare cancers: a meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2022. http://dx.doi.org/10.37766/inplasy2022.2.0081.

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Review question / Objective: Whether the application of adaptive design in clinical trials of rare cancers can shorten the duration of clinical trials? Condition being studied: Currently, the development of innovative drug products (InMPs) for rare cancers faces many challenges, including the difficulty of enrolling sufficient numbers of patients from small and heterogeneous patient populations for clinical trials, and the significant risks of high financial investment, long development times and potential failure from a pharmaceutical company's perspective for rare cancer drugs due to limited knowledge of the natural history of the disease. Therefore, alternative approaches to clinical trial design are needed to conduct cost-effective, well-controlled analyses that can assess treatment effects in small, heterogeneous populations within shorter time frames. Adaptive trials, on the other hand, may be an effective solution to this problem. Adaptive clinical trials are designed to accelerate the clinical trial process by making predefined adjustments to key parameters through data accumulated at predefined time points during the trial without compromising the integrity and validity of the results.This study aims to examine the value of adaptive design in reducing the duration of clinical trials in rare cancers and encourage their wider implementation.
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Keith, B., A. Apostolatos, A. Kodakkal, R. Rossi, R. Tosi, B. Wohlmuth, and C. Soriano. D2.3. Adjoint-based error estimation routines. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.022.

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This document presents a simple and ecient strategy for adaptive mesh renement (AMR) and a posteriori error estimation for the transient incompressible Navier{Stokes equations. This strategy is informed by the work of Prudhomme and Oden [22, 23] as well as modern goal-oriented methods such as [5]. The methods described in this document have been implemented in the Kratos Multiphysics software and uploaded to https://zenodo.org [27].1 This document includes: A review of the state-of-the-art in solution-oriented and goal-oriented AMR. The description of a 2D benchmark model problem of immediate relevance to the objectives of the ExaQUte project. The denition and a brief mathematical summary of the error estimator(s). The results obtained. A description of the API.
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Saltzman, J. Patched based methods for adaptive mesh refinement solutions of partial differential equations. Office of Scientific and Technical Information (OSTI), September 1997. http://dx.doi.org/10.2172/584924.

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Webster, Clayton G., Guannan Zhang, and Max D. Gunzburger. An adaptive wavelet stochastic collocation method for irregular solutions of stochastic partial differential equations. Office of Scientific and Technical Information (OSTI), October 2012. http://dx.doi.org/10.2172/1081925.

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Whitfield, Paula, Jenny Davis, Amanda Tritinger, Danielle Szimanski, Rebecca Golden, Joseph Gailani, Michael Ramirez, Brook Herman, Matt Whitbeck, and Jeffery King. Swan Island : monitoring and adaptive management plan. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/45044.

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Swan Island is a 10.12 ha island located in the Maryland waters of the Chesapeake Bay. Because of its value as a natural wave break for the town of Ewell on nearby Smith Island, as well as the ongoing erosion and subsidence of the island, in 2019 US Army Corps of Engineers (USACE)–Baltimore District placed 45,873 m³ of dredged sediment and planted 200,000 marsh plants. This restoration provided an opportunity to quantify the engineering (that is, resilience) and ecological performance of the island, postplacement. The lack of quantitative data on the performance of natural features such as islands has led to perceived uncertainties that are often cited as barriers to implementation. To address these data gaps, a multidisciplinary collaboration of five government entities identified project objectives and monitoring parameters through a series of mediated workshops and then developed a conceptual model to articulate those parameters and the linkages between them. This monitoring and adaptive management plan (MAMP) documents those monitoring parameters and procedures and can serve as an example for other scales, regions, and research questions. Documenting research and monitoring efforts may help to foster widespread acceptance of nature-based solutions such as islands.
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