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Добірка наукової літератури з теми "Mathematical models"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 9 червня 2025

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Статті в журналах з теми "Mathematical models"

Gardiner, Tony, and Gerd Fischer. "Mathematical Models." Mathematical Gazette 71, no. 455 (March 1987): 94. http://dx.doi.org/10.2307/3616334.

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Pavankumari, V. "Mathematical and Stochastic Growth Models." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1576–82. http://dx.doi.org/10.22214/ijraset.2021.39055.

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Abstract: Many statistical and mathematical models of growth are developed in the literature and effectively applied to various conditions in the existent world that involve many research problems in the different fields of applied statistics. Nevertheless, still, there is an equally large number of conditions, which have not yet been mathematically or statistically modeled, due to the complex situations or formed models are mathematically or statistically inflexible. The present study is based on mathematical and stochastic growth models. The specification of both the growth models is depicted. A detailed study of newly modified growth models is mentioned. This research will give substantial information on growth models, such as proposed modified exponential growth models and their specifications clearly motioned which gives scope for future research.

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Kumari, V. Pavan, Venkataramana Musala, and M. Bhupathi Naidu. "Mathematical and Stochastic Growth Models." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 987–89. http://dx.doi.org/10.22214/ijraset.2022.42330.

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Abstract: Many statistical and mathematical models of growth are developed in the literature and effectively applied to various conditions in the existent world involves many research problems in the different fields of applied statistics. Nevertheless, still, there are an equally a large number of conditions, which have not yet been mathematically or statistically modeled, due to the complex situations or formed models are mathematically or statistically inflexible. The present study is based on mathematical and stochastic growth models. The specification of both the growth models is depicted. A details study of newly modified growth models are mentioned. This research will give substantial information on growth models, such as proposed modified exponential growth models and it’s specifications clearly motioned which gives scope for future research.

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Denton, Brian, Pam Denton, and Peter Lorimer. "Making Mathematical Models." Mathematical Gazette 78, no. 483 (November 1994): 364. http://dx.doi.org/10.2307/3620232.

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Suzuki, Takashi. "Mathematical models of tumor growth systems." Mathematica Bohemica 137, no. 2 (2012): 201–18. http://dx.doi.org/10.21136/mb.2012.142866.

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Kogalovsky, M. R. "Digital Libraries of Economic-Mathematical Models: Economic-Mathematical and Information Models." Market Economy Problems, no. 4 (2018): 89–97. http://dx.doi.org/10.33051/2500-2325-2018-4-89-97.

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Banasiak, J. "Kinetic models – mathematical models of everything?" Physics of Life Reviews 16 (March 2016): 140–41. http://dx.doi.org/10.1016/j.plrev.2016.01.005.

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Kleiner, Johannes. "Mathematical Models of Consciousness." Entropy 22, no. 6 (May 30, 2020): 609. http://dx.doi.org/10.3390/e22060609.

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In recent years, promising mathematical models have been proposed that aim to describe conscious experience and its relation to the physical domain. Whereas the axioms and metaphysical ideas of these theories have been carefully motivated, their mathematical formalism has not. In this article, we aim to remedy this situation. We give an account of what warrants mathematical representation of phenomenal experience, derive a general mathematical framework that takes into account consciousness’ epistemic context, and study which mathematical structures some of the key characteristics of conscious experience imply, showing precisely where mathematical approaches allow to go beyond what the standard methodology can do. The result is a general mathematical framework for models of consciousness that can be employed in the theory-building process.

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Byrne, Patrick, S. D. Howison, F. P. Kelly, and P. Wilmott. "Mathematical Models in Finance." Statistician 45, no. 3 (1996): 389. http://dx.doi.org/10.2307/2988481.

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Kozhanov, V. S., S. O. Ustalkov, and A. O. Khudoshina. "TOW CABLES MATHEMATICAL MODELS." Mathematical Methods in Technologies and Technics, no. 5 (2022): 62–68. http://dx.doi.org/10.52348/2712-8873_mmtt_2022_5_62.

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Дисертації з теми "Mathematical models"

Tonner, Jaromír. "Overcomplete Mathematical Models with Applications." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2010. http://www.nusl.cz/ntk/nusl-233893.

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Chen, Donoho a Saunders (1998) studují problematiku hledání řídké reprezentace vektorů (signálů) s použitím speciálních přeurčených systémů vektorů vyplňujících prostor signálu. Takovéto systémy (někdy jsou také nazývány frejmy) jsou typicky vytvořeny buď rozšířením existující báze, nebo sloučením různých bazí. Narozdíl od vektorů, které tvoří konečně rozměrné prostory, může být problém formulován i obecněji v rámci nekonečně rozměrných separabilních Hilbertových prostorů (Veselý, 2002b; Christensen, 2003). Tento funkcionální přístup nám umožňuje nacházet v těchto prostorech přesnější reprezentace objektů, které, na rozdíl od vektorů, nejsou diskrétní. V této disertační práci se zabývám hledáním řídkých representací v přeurčených modelech časových řad náhodných veličin s konečnými druhými momenty. Numerická studie zachycuje výhody a omezení tohoto přístupu aplikovaného na zobecněné lineární modely a na vícerozměrné ARMA modely. Analýzou mnoha numerických simulací i modelů reálných procesů můžeme říci, že tyto metody spolehlivě identifikují parametry blízké nule, a tak nám umožňují redukovat původně špatně podmíněný přeparametrizovaný model. Tímto významně redukují počet odhadovaných parametrů. V konečném důsledku se tak nemusíme starat o řády modelů, jejichž zjišťování je většinou předběžným krokem standardních technik. Pro kratší časové řady (100 a méně vzorků) řídké odhady dávají lepší predikce v porovnání s těmi, které jsou založené na standardních metodách (např. maximální věrohodnosti v MATLABu - MATLAB System Identification Toolbox (IDENT)). Pro delší časové řady (500 a více) obě techniky dávají v podstatě stejně přesné predikce. Na druhou stranu řešení těchto problémů je náročnější, a to i časově, nicméně výpočetní doba je stále přijatelná.

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Widmer, Tobias K. "Reusable mathematical models." Zürich : ETH, Eidgenössische Technische Hochschule Zürich, Department of Computer Science, Chair of Software Engineering, 2004. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=192.

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Maggiori, Claudia. "Mathematical models in biomedicine." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21247/.

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In questa tesi vengono innanzitutto presentati due metodi matematici per lo studio di modelli biomedici e comportamentali. I modelli presentati sono tre: un modello per lo studio dell'evoluzione della malattia di Alzheimer, uno per lo studio dello sviluppo dei tumori e uno per la diffusione del Covid-19. Si riportano anche alcuni codici utilizzati per lo studio e lo sviluppo dei modelli trattati. Le conclusioni contengono alcuni possibili sviluppi degli argomenti trattati.

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Mathewson, Donald Jeffrey. "Mathematical models of immunity." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29575.

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A cross-linking model for the activation of the A cell or immune accessory cell as a function of certain extracellular conditions is developed to determine the valency of the specific factor receptor on the A cell surface. It is found that such a determination can be made based on the FWHM of cross-linking curves which differ by a full order of magnitude between the bivalent receptor case and the monovalent receptor case. This determination can be made provided one can obtain accurate values for the equilibrium constants which characterize the system and provided that activation and IL-1 secretion is a linear function of cross-linking. It is also found that a determination of valence can be made if the equilibrium constants are such that substantial one receptor bridge formation takes place (one antibody molecule bound on both ends by the same receptor). This one-receptor bridge formation only takes place if the receptor is bivalent, and it presents itself in the cross-linking curve in a very distinctive manner. A second network model described as an ecological competition model of steady state lymphocyte populations is presented. This model, known as the symmetrical network theory is analysed numerically by integration of the differential equations and shown to provide a reasonable qualitative picture of the immune system's stable steady states, and offer a glimpse of state switching. Science, Faculty of Physics and Astronomy, Department of Graduate

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Heron, Dale Robert. "Mathematical models of superconductivity." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296893.

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Bozic, Ivana. "Mathematical Models of Cancer." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10220.

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Major efforts to sequence cancer genomes are now occurring throughout the world. Though the emerging data from these studies are illuminating, their reconciliation with epidemiologic and clinical observations poses a major challenge. Here we present mathematical models that begin to address this challenge. First we present a model of accumulation of driver and passenger mutations during tumor progression and derive a formula for the number of driver mutations as a function of the total number of mutations in a tumor. Fitting this formula to recent experimental data, we were able to calculate the selective advantage provided by a typical driver mutation. Second, we performed a quantitative analysis of pancreatic cancer metastasis genetic data. The results of this analysis define a broad time window for detection of pancreatic cancer before metastatic dissemination. Finally, we model the evolution of resistance to targeted cancer therapy. We apply our model to experimental data on the response to panitumumab, targeted therapy against colorectal cancer. Our modeling suggested that cells resistant to therapy were likely present in patients’ tumors prior to the start of therapy. Mathematics

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Luther, Roger. "Mathematical models of kleptoparasitism." Thesis, University of Sussex, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410365.

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The phenomenon of kleptoparasitism - "food-stealing" - has frequently been observed, in a wide range of animal species. In this thesis, I extend the game-theoretic model of kleptoparasitism, proposed by Broom and Ruxton 1998, in a number of ways. Firstly, using their model, I investigate how quickly the equilibrium state of a kleptoparasitic population is reached. This work has been published (Luther and Broom 2004). I then investigate the case of a single homogenous population of kleptoparasites, finding which behaviours are Evolutionarily Stable Strategies. This is done with a variable probability that a challenger succeeds when attempting to steal food from a handler, and also allowing the possibility that the handler does not resist the attack. This work has been published (Broom et al 2004) I then consider populations of two groups, one stealing and the other only foraging, to find ESS's, particularly looking at situations where a mixed population can be an ESS, and other cases where pure populations are an ESS. I do this for indistinguishable groups, and then distinguishable groups. I show that a homogenous facultative population behaving in the Broom and Ruxton 1998 ESS has the same handling ratio as a mixed obligate population of kleptoparasites and foragers. Finally, I discuss some ornithological data on kleptoparasitism, and make a simple comparison with our models, to see if they are an accurate representation of the actual phenomenon

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Mazzag, Barbara Cathrine. "Mathematical models in biology /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2002. http://uclibs.org/PID/11984.

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Niederhauser, Beat. "Mathematical Aspects of Hopfield models." [S.l.] : [s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=960147535.

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Kowalewski, Jacob. "Mathematical Models in Cellular Biophysics." Licentiate thesis, KTH, Applied Physics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4361.

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Cellular biophysics deals with, among other things, transport processes within cells. This thesis presents two studies where mathematical models have been used to explain how two of these processes occur.Cellular membranes separate cells from their exterior environment and also divide a cell into several subcellular regions. Since the 1970s lateral diffusion in these membranes has been studied, one the most important experimental techniques in these studies is fluorescence recovery after photobleach (FRAP). A mathematical model developed in this thesis describes how dopamine 1 receptors (D1R) diffuse in a neuronal dendritic membrane. Analytical and numerical methods have been used to solve the partial differential equations that are expressed in the model. The choice of method depends mostly on the complexity of the geometry in the model.Calcium ions (Ca2+) are known to be involved in several intracellular signaling mechanisms. One interesting concept within this field is a signaling microdomain where the inositol 1,4,5-triphosphate receptor (IP3R) in the endoplasmic reticulum (ER) membrane physically interacts with plasma membrane proteins. This microdomain has been shown to cause the intracellular Ca2+ level to oscillate. The second model in this thesis describes a signaling network involving both ER membrane bound and plasma membrane Ca2+ channels and pumps, among them store-operated Ca2+ (SOC) channels. A MATLAB® toolbox was developed to implement the signaling networks and simulate its properties. This model was also implemented using Virtual cell.The results show a high resemblance between the mathematical model and FRAP data in the D1R study. The model shows a distinct difference in recovery characteristics of simulated FRAP experiments on whole dendrites and dendritic spines, due to differences in geometry. The model can also explain trapping of D1R in dendritic spines.The results of the Ca2+ signaling model show that stimulation of IP3R can cause Ca2+ oscillations in the same frequency range as has been seen in experiments. The removing of SOC channels from the model can alter the characteristics as well as qualitative appearance of Ca2+ oscillations. Cellulär biofysik behandlar bland annat transportprocesser i celler. I denna avhandling presenteras två studier där matematiska modeller har använts för att förklara hur två av dess processer uppkommer.Cellmembran separerar celler från deras yttre miljö och delar även upp en cell i flera subcellulära regioner. Sedan 1970-talet har lateral diffusion i dessa membran studerats, en av de viktigaste experimentella metoderna i dessa studier är fluorescence recovery after photobleach (FRAP). En matematisk modell utvecklad i denna avhandling beskriver hur dopamin 1-receptorer (D1R) diffunderar i en neural dendrits membran. Analytiska och numeriska metoder har använts för att lösa de partiella differentialekvationer som uttrycks i modellen. Valet av metod beror främst på komplexiteten hos geometrin i modellen.Kalciumjoner (Ca2+) är kända för att ingå i flera intracellulära signalmekanismer. Ett intressant koncept inom detta fält är en signalerande mikrodomän där inositol 1,4,5-trifosfatreceptorn (IP3R) i endoplasmatiska nätverksmembranet (ER-membranet) fysiskt interagerar med proteiner i plasmamembranet. Denna mikrodomän har visats vara orsak till oscillationer i den intracellulära Ca2+-nivån. Den andra modellen i denna avhandling beskriver ett signalerande nätverk där både Ca2+-kanaler och pumpar bundna i ER-membranet och i plasmamembranet, däribland store-operated Ca2+(SOC)-kanaler, ingår. Ett MATLAB®-verktyg utvecklades för att implementera signalnätverket och simulera dess egenskaper. Denna modell implementerades även i Virtual cell.Resultaten visar en stark likhet mellan den matematiska modellen och FRAP-datat i D1R-studien. Modellen visar en distinkt skillnad i återhämtningsegenskaper hos simulerade FRAP-experiment på hela dendriter och dendritiska spines, beroende på skillnader i geometri. Modellen kan även förklara infångning av D1R i dendritiska spines.Resultaten från Ca2+-signaleringmodellen visar att stimulering av IP3R kan orsaka Ca2+-oscillationer inom samma frekvensområde som tidigare setts i experiment. Att ta bort SOC-kanaler från modellen kan ändra karaktär hos, såväl som den kvalitativa uppkomsten av Ca2+-oscillationer.

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Книги з теми "Mathematical models"

Fischer, Gerd, ed. Mathematical Models. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-18865-8.

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Tanguy, Jean-Michel, ed. Mathematical Models. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9781118557853.

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Ershov, I͡Uriĭ Leonidovich. Constructive models. New York: Consultants Bureau, 2000.

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Hrsg, Crampin Mike, ed. Mathematical models and methods: Mathematical modelling. Milton Keynes: Open University, 1993.

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Torres, Pedro J. Mathematical Models with Singularities. Paris: Atlantis Press, 2015. http://dx.doi.org/10.2991/978-94-6239-106-2.

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Borisov, Andrey Valerievich, and Anatoly Vlasovich Chigarev. Mathematical Models of Exoskeleton. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97733-7.

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Stamova, Ivanka, and Gani Stamov. Applied Impulsive Mathematical Models. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28061-5.

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Mayergoyz, I. D. Mathematical Models of Hysteresis. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3028-1.

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Ansorge, Rainer. Mathematical Models of Fluiddynamics. Weinheim, FRG: Wiley-VCH Verlag GmbH & Co. KGaA, 2002. http://dx.doi.org/10.1002/3527602771.

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Zazzu, Valeria, Maria Brigida Ferraro, and Mario R. Guarracino, eds. Mathematical Models in Biology. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23497-7.

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Частини книг з теми "Mathematical models"

Holst, Niels. "Mathematical Models." In Decision Support Systems for Weed Management, 3–23. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44402-0_1.

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Gross, Sven, and Arnold Reusken. "Mathematical models." In Springer Series in Computational Mathematics, 33–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19686-7_2.

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Pulido-Bosch, Antonio. "Mathematical Models." In Principles of Karst Hydrogeology, 195–240. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55370-8_6.

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Hinrichsen, Diederich, and Anthony J. Pritchard. "Mathematical Models." In Mathematical Systems Theory I, 1–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-26410-8_1.

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Marquardt, Wolfgang, Jan Morbach, Andreas Wiesner, and Aidong Yang. "Mathematical Models." In OntoCAPE, 323–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04655-1_9.

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Mauergauz, Yuri. "Mathematical Models." In Advanced Planning and Scheduling in Manufacturing and Supply Chains, 43–87. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27523-9_2.

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Skiena, Steven S. "Mathematical Models." In Texts in Computer Science, 201–36. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55444-0_7.

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Thorn, Colin E. "Mathematical models." In An Introduction to Theoretical Geomorphology, 193–212. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-010-9441-2_13.

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Layer, Edward. "Mathematical Models." In Modelling of Simplified Dynamical Systems, 3–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56098-9_2.

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Payne, Stephen. "Mathematical Models." In Cerebral Autoregulation, 39–56. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31784-7_3.

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Тези доповідей конференцій з теми "Mathematical models"

Morrow, Gregory J., and Wei-Shih Yang. "Probability Models in Mathematical Physics." In Conference on Probability Models in Mathematical Physics. WORLD SCIENTIFIC, 1991. http://dx.doi.org/10.1142/9789814539852.

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Weckesser, Markus, Malte Lochau, Michael Ries, and Andy Schürr. "Mathematical Programming for Anomaly Analysis of Clafer Models." In MODELS '18: ACM/IEEE 21th International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3239372.3239398.

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Tweedie, Lisa, Robert Spence, Huw Dawkes, and Hus Su. "Externalising abstract mathematical models." In the SIGCHI conference. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/238386.238587.

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Li, Yajun. "Mathematical models for diode laser beams." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.thr5.

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It is known that there are two mathematical models for the elliptic beams generated by semi conductor laser diodes. The first model is the simple Gaussian model,1 in which two Gaussian distributions with different widths are employed to describe the light distribution over the elliptic cross-section of the beam. The second model is known as the Lorentzian-Gaussian model2 which was established in a study of the fact that the Gaussian distribution is valid only for the light field parallel to the junction and in the perpendicular direction the field is described by the Loretzian distribution. In this paper, numerical examples are presented to clarify which one of these two models fits the measurements better, and it is shown that the simple Gaussian model has a certain advantage over the Lorentzian-Gaussian model.

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Maskal, Alan B., and Fatih Aydogan. "Mathematical Models of Spacer Grids." In 2016 24th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/icone24-60098.

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The fuel rods in Pressurized Water Reactor (PWR) and Boiling Water Reactor (BWR) cores are supported by spacer grids. Even though spacer grids add to the pressure loss in the reactor core, spacer grids have several benefits in Light Water Reactors (LWRs). Some of these benefits are: (i) increasing the turbulence at the bottom of the reactor core for better heat transfer in single phase region of the LWRs, (ii) improving the departure nucleate boiling ratio results for PWRs, and (iii) improving critical power ratio (CPR) values by increasing the thickness of film in annular flow regime in the top section of the reactor core of BWRs. Several mathematical models have been developed for single and two phase pressure loss across the grid spacer. Almost all of them significantly depend on Reynolds Number. Spacer designs have evolved (incorporating mixing vanes, springs, dimples, etc), resulting in the complexity of the analysis across the grid, all the models have been compared not only theoretically but also quantitatively. For the quantitative comparisons, this work compares the results of mathematical spacer models with experimental data of BWR Full Size Fine Mesh Bundle Tests (BFBT). The experimental data of BFBT provides very detailed experimental results for pressure drop by using several different boundary condition and detailed pressure drop measurements. Since one CT-scanner was used at the bundle exit and three X-ray densitometers were used for the chordal average void distribution at different elevations to generate the BFBT results, detailed two phase parameters have been measured in BFBT database. Two bundle types of BFBT, the current 8×8 type and the high burn-up 8×8 type, were simulated. Three combinations of radial and axial power shapes were tested: 1) beginning of cycle (BOC) radial power pattern/cosine axial power shape (the C2A pattern); 2) end of cycle (EOC) radial power pattern/cosine axial power shape (C2B pattern); and 3) beginning of cycle radial power pattern/inlet peaked axial power shape (C3 pattern) in BFBT. The pressure drop in BFBT database was measured in both single-phase flow and two-phase flow conditions that cover the normal operational behavior. BFBT database gives the three combinations of high burnup assemblies with different radial and axial power shapes, namely C2A, C2B and C3, which were utilized in the critical power measurements. There are two types of spacers in this program — ferrule type and grid type. Therefore, detailed experimental data of BFBT was used for analyzing mathematical models of spacer grid for various boundary conditions of BWR in this paper. It was observed and discussed that pressure drop values due to spacer models can be significantly different.

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Chilbert, M., J. Myklebust, T. Prieto, T. Swiontek, and A. Sances. "Mathematical models of electrical injury." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1988. http://dx.doi.org/10.1109/iembs.1988.94632.

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Bogdanov, Yu I., A. Yu Chernyavskiy, A. S. Holevo, V. F. Lukichev, and A. A. Orlikovsky. "Mathematical models of quantum noise." In International Conference on Micro-and Nano-Electronics 2012, edited by Alexander A. Orlikovsky. SPIE, 2013. http://dx.doi.org/10.1117/12.2017396.

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Nedostup, Leonid, Yuriy Bobalo, Myroslav Kiselychnyk, and Oxana Lazko. "Production Systems Complex Mathematical Models." In 2007 9th International Conference - The Experience of Designing and Applications of CAD Systems in Microelectronics. IEEE, 2007. http://dx.doi.org/10.1109/cadsm.2007.4297505.

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Sanjana, N., M. S. Deepthi, H. R. Shashidhara, and Yajunath Kaliyath. "Comparison of Memristor Mathematical Models." In 2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER). IEEE, 2022. http://dx.doi.org/10.1109/discover55800.2022.9974669.

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Dowding, Kevin. "Quantitative Validation of Mathematical Models." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/htd-24308.

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Abstract Validation is a process to compare a mathematical model with a set of physical experiment to quantify the accuracy of the model to represent the physical world (experiment). Because the goal is to use experiments to quantify the accuracy of the mathematical model, the interaction of the model and experiment must be carefully studied. Advancing the comparison beyond a qualitative nature requires consideration of the errors in the process and the effect of these errors on the comparison. The mathematical model, in conjunction with sensitivity analysis, uncertainty analysis, and statistical analysis are tools for studying the interaction of the model and experiment and quantifying the effect of errors. A model for steady state heat conduction is used to discuss issues associated with the errors in the validation process and demonstrate a quantitative process to study validation of mathematical models.

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Звіти організацій з теми "Mathematical models"

Equihua, M., and O. Perez-Maqueo. Mathematical Modeling and Conservation. American Museum of Natural History, 2010. http://dx.doi.org/10.5531/cbc.ncep.0154.

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Formal models are indispensable tools in natural resource management and in conservation biology. Explicit modeling can be a helpful tool for studying these systems, communicating across disciplines, and integrating varying viewpoints of numerous stakeholders. This module demonstrates how to explicitly construct models as alternative representations to help interpret and understand nature. Through a synthesis and two exercises, it describes the general context of scientific modeling (i.e., use and types of models), and allows students to practice building a model by evaluating the relationship between rabbit and fox population dynamics - from stating the problem, constructing a dynamic hypothesis, and formulating and testing the model.

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Mayergoyz, I. D. [Mathematical models of hysteresis]. Office of Scientific and Technical Information (OSTI), January 1991. http://dx.doi.org/10.2172/6911694.

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Mayergoyz, I. D. Mathematical models of hysteresis. Office of Scientific and Technical Information (OSTI), September 1992. http://dx.doi.org/10.2172/6946876.

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Mayergoyz, I. Mathematical models of hysteresis. Office of Scientific and Technical Information (OSTI), August 1989. http://dx.doi.org/10.2172/5246564.

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Kaper, H. Mathematical models of superconductivity. Office of Scientific and Technical Information (OSTI), March 1991. http://dx.doi.org/10.2172/5907100.

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Ringhofer, Christian. Mathematical Models for VLSI Device Simulation. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada191125.

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Lovianova, Iryna V., Dmytro Ye Bobyliev, and Aleksandr D. Uchitel. Cloud calculations within the optional course Optimization Problems for 10th-11th graders. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3267.

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The article deals with the problem of introducing cloud calculations into 10th-11th graders’ training to solve optimization problems in the context of the STEM-education concept. After analyzing existing programmes of optional courses on optimization problems, the programme of the optional course Optimization Problems has been developed and substantiated implying solution of problems by the cloud environment CoCalc. It is a routine calculating operation and not a mathematical model that is accentuated in the programme. It allows considering more problems which are close to reality without adapting the material while training 10th-11th graders. Besides, the mathematical apparatus of the course which is partially known to students as the knowledge acquired from such mathematics sections as the theory of probability, mathematical statistics, mathematical analysis and linear algebra is enough to master the suggested course. The developed course deals with a whole class of problems of conventional optimization which vary greatly. They can be associated with designing devices and technological processes, distributing limited resources and planning business functioning as well as with everyday problems of people. Devices, processes and situations to which a model of optimization problem is applied are called optimization problems. Optimization methods enable optimal solutions for mathematical models. The developed course is noted for building mathematical models and defining a method to be applied to finding an efficient solution.

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Mayergoyz, Isaak. MATHEMATICAL MODELS OF HYSTERESIS (DYNAMIC PROBLEMS IN HYSTERESIS). Office of Scientific and Technical Information (OSTI), August 2006. http://dx.doi.org/10.2172/889747.

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Dawson, Steven. The Genesis of Cyberscience and its Mathematical Models (CYBERSCIENCE). Fort Belvoir, VA: Defense Technical Information Center, February 2005. http://dx.doi.org/10.21236/ada431570.

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Steefel, C., D. Moulton, G. Pau, K. Lipnikov, J. Meza, P. Lichtner, T. Wolery, et al. Mathematical Formulation Requirements and Specifications for the Process Models. Office of Scientific and Technical Information (OSTI), November 2010. http://dx.doi.org/10.2172/1000859.

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