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Relevant bibliographies by topics / Mathematical modeling

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Academic literature on the topic 'Mathematical modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Mathematical modeling"

1

Mitra, Novelyn L., Ma Jobelle R. David, and Rommel Pariñas Deus Gleena P. Pascual. "Predictive Modeling for Criminology Licensure Examination Success Through Mathematical Modelling." International Journal of Research Publication and Reviews 5, no. 3 (2024): 168–76. http://dx.doi.org/10.55248/gengpi.5.0324.0604.

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Khurazovna, Aymatova Farida, and Shamsiyev Damin Najmiddinovich. "Mathematical Modeling of Company Activity." American Journal of Applied Science and Technology 5, no. 3 (2025): 38–41. https://doi.org/10.37547/ajast/volume05issue03-07.

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The article discusses the tasks of mathematical modeling of economic processes, particularly focusing on the mathematical modeling of tourism company activities. Under given conditions, a mathematical model is constructed, an optimal solution is found, and the results are analyzed.

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Guha, Probal, and Vaishnavi Unde. "Mathematical Modeling of Spiral Heat Exchanger." International Journal of Engineering Research 3, no. 4 (2014): 226–29. http://dx.doi.org/10.17950/ijer/v3s4/409.

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Latysheva, O., and Yu Chupryna. "Economic and Mathematical Modeling in Budgeting." Economic Herald of the Donbas, no. 4 (74) (2023): 32–36. http://dx.doi.org/10.12958/1817-3772-2023-4(74)-32-36.

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The article is devoted to an overview of modern modeling approaches for effective management of enterprise budgets. The article examines the toolkit of economic and mathematical modeling that can be used in the budgeting system. It is proposed to increase the efficiency of the budgeting process by applying the tools of economic and mathematical modeling at the stages of budget development and resource allocation, as well as in the process of budget control and monitoring. To increase the clarity of the simulation procedure and results, a visualization of the TO BE model is presented in IDF0 no

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Ibroximov, Baxtiyor Toyirjon o'g'li, Rejabboy Valijon oʻgʻli Maʼrufjonov, and Saidakbar Oybek oʻgʻli Gʻulomov. "MODELING OF MATHEMATICAL PHYSICS EQUATIONS." Academic Research Journal 2, no. 2 (2023): 112–18. https://doi.org/10.5281/zenodo.7836068.

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Many processes in modern physics are explained by solving the equations of mathematical physics. These equations are nonlinear differential equations that cannot always be solved analytically. In this work, we solved the equations of mathematical physics using the software package "wolfram mathematica" and analyzed the results.

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Longo, R. T. "Mathematical modeling technique." AIP Advances 9, no. 12 (2019): 125211. http://dx.doi.org/10.1063/1.5129638.

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Gerstenschlager, Natasha E., and Katherine Ariemma Marin. "GPS: Mathematical Modeling." Mathematics Teacher: Learning and Teaching PK-12 115, no. 9 (2022): 668–73. http://dx.doi.org/10.5951/mtlt.2022.0128.

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Growing Problem Solvers provides four original, related, classroom-ready mathematical tasks, one for each grade band. Together, these tasks illustrate the trajectory of learners’ growth as problem solvers across their years of school mathematics.

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Haris, Denny. "USING VIRTUAL LEARNING ENVIRONMENT ON REALISTIC MATHEMATICS EDUCATION TO ENHANCE SEVENTH GRADERS’ MATHEMATICAL MODELING ABILITY." SCHOOL EDUCATION JOURNAL PGSD FIP UNIMED 12, no. 2 (2022): 152–59. http://dx.doi.org/10.24114/sejpgsd.v12i2.35387.

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Many research studied that realistic mathematics education (RME) can be an alternative solution to students’ difficulties in learning mathematics. Various forms of technology additionally are further employed to support students' mathematical achievements. However, research on the implementation of virtual learning environments (VLE) with the RME approach is still lacking. The main goals of this research were to create an instructional process of virtual learning environments on realistic mathematics education to improve seventh graders' mathematical modeling abilities and to examine the effec

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Zarubin, V. S., and E. S. Sergeeva. "Mathematical modeling of structural-sensitive nanocomposites deformation." Computational Mathematics and Information Technologies 2, no. 1 (2018): 17–24. http://dx.doi.org/10.23947/2587-8999-2018-2-1-17-24.

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Kumar, Rakesh, Bharti Saxena, Ritu Shrivastava, and Ramakant Bhardwaj. "Mathematical Modeling of Dengue Disease Transmission Dynamics." Indian Journal Of Science And Technology 17, no. 39 (2024): 4101–10. http://dx.doi.org/10.17485/ijst/v17i39.1526.

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Objectives: This study developed a compartmental ordinary differential equation model to investigate dengue transmission dynamics within a human population. The model stratified the population into susceptible, exposed, infected, and recovered classes, incorporating key epidemiological factors. Methods: Model equilibrium analysis was conducted to determine the stability of disease-free and endemic states. The basic reproduction number (R₀) was calculated to quantify the potential for disease spread. Additionally, sensitivity analysis was performed to assess the impact of key parameters on mode

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Dissertations / Theses on the topic "Mathematical modeling"

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Beauchamp, Robert Edward. "Mathematical modeling using Maple." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1996. http://handle.dtic.mil/100.2/ADA319951.

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Phillips, Donovan D. "Mathematical modeling using MATLAB." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1998. http://handle.dtic.mil/100.2/ADA358796.

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Thesis (M.S. in Applied Mathematics) Naval Postgraduate School, December 1998.<br>"December 1998." Thesis advisor(s): Maurice D. Weir. Includes bibliographical references (p. 121). Also available online.

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Green, Terrell J. "Mathematical modeling of fire /." The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487331541710161.

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Pillutla, R. R. "Mathematical modeling of biosystems." Thesis(Ph.D.), CSIR-National Chemical Laboratory, Pune, 1991. http://dspace.ncl.res.in:8080/xmlui/handle/20.500.12252/3013.

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Wilmer, Archie. "Javelin analysis using mathematical modeling." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA283466.

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Thesis (M.S. in Applied Mathematics) Naval Postgraduate School, June 1994.<br>Thesis advisor(s): Bard K. Mansager, Maurice D. Weir. "June 1994." Includes bibliographical references. Also available online.

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Pratikakis, Nikolaos. "Mathematical modeling of rail gun." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Sep%5FPratikakis.pdf.

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Thesis (M.S. in Mechanical Engineering)--Naval Postgraduate School, September 2006.<br>Thesis Advisor(s): Kwon Young. "September 2006." Includes bibliographical references (p. 77-78). Also available in print.

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Weens, William. "Mathematical modeling of liver tumor." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2012. http://tel.archives-ouvertes.fr/tel-00779177.

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Comme démontre récemment pour la régénération du foie après un dommage cause par intoxication, l'organisation et les processus de croissance peuvent être systématiquement analyses par un protocole d'expériences, d'analyse d'images et de modélisation [43]. Les auteurs de [43] ont quantitativement caractérise l'architecture des lobules du foie, l'unité fonctionnelle fondamentale qui constitue le foie, et en ont conçu un modèle mathématique capable de prévoir un mécanisme jusqu'alors inconnu de division ordonnée des cellules. La prédiction du modèle fut ensuite validée expérimentalement. Dans ce

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Blomqvist, Oscar, Sebastian Bremberg, and Richard Zauer. "Mathematical modeling of flocking behavior." Thesis, KTH, Optimeringslära och systemteori, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-103812.

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In this thesis, the ocking behaviour of prey when threatened by a group of predators, is investigated using dynamical systems. By implementing the unicycle model, a simulation is created using Simulink and Matlab. A set of forces are set up to describe the state of the prey, that in turn determines their behaviour in dierent scenarios. An eective strategy is found so all members of the ock can survive the predator attack, taking into account the advantages of the predator's greater translational velocity and the prey's higher angular velocity. Multiple obstacles and an energy constraint are ad

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Keller, Peter. "Mathematical modeling of molecular motors." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6304/.

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Amongst the many complex processes taking place in living cells, transport of cargoes across the cytosceleton is fundamental to cell viability and activity. To move cargoes between the different cell parts, cells employ Molecular Motors. The motors operate by transporting cargoes along the so-called cellular micro-tubules, namely rope-like structures that connect, for instance, the cell-nucleus and outer membrane. We introduce a new Markov Chain, the killed Quasi-Random-Walk, for such transport molecules and derive properties like the maximal run length and time. Furthermore we introduce permu

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Kleinstreuer, Nicole Churchill. "Mathematical modeling of renal autoregulation." Thesis, University of Canterbury. Bioengineering, 2009. http://hdl.handle.net/10092/2532.

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Renal autoregulation is unique and critically important in maintaining homeostasis in the body via control of renal blood flow and filtration. The myogenic reflex responds directly to pressure variation and is present throughout the vasculature in varying degrees, while the tubuloglomerular feedback (TGF) mechanism adjusts microvascular resistance and glomerular filtration rate (GFR) to maintain distal tubular NaCl delivery. No simple models are available which allow the independent contributions of the myogenic and TGF responses to be compared and which include control over multiple metabolic

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Books on the topic "Mathematical modeling"

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McDuffie, Amy Roth, ed. Mathematical Modeling and Modeling Mathematics. National Council of Teachers of Mathematics, 2016.

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Palacios, Antonio. Mathematical Modeling. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04729-9.

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Heinz, Stefan. Mathematical Modeling. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20311-4.

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Eck, Christof, Harald Garcke, and Peter Knabner. Mathematical Modeling. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55161-6.

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Uvarova, Ludmila A., and Anatolii V. Latyshev, eds. Mathematical Modeling. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3397-6.

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Phi Delta Kappa. Educational Foundation., ed. Mathematical modeling. Phi Delta Kappa Educational Foundation, 1995.

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Meerschaert, Mark M. Mathematical modeling. 2nd ed. Academic Press, 1999.

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Meerschaert, Mark M. Mathematical modeling. 3rd ed. Elsevier Academic Press, 2007.

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Lesh, Richard, Peter L. Galbraith, Christopher R. Haines, and Andrew Hurford, eds. Modeling Students' Mathematical Modeling Competencies. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6271-8.

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Lesh, Richard, Peter L. Galbraith, Christopher R. Haines, and Andrew Hurford, eds. Modeling Students' Mathematical Modeling Competencies. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-0561-1.

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Book chapters on the topic "Mathematical modeling"

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Mahlke, Debora. "Mathematical Modeling." In A Scenario Tree-Based Decomposition for Solving Multistage Stochastic Programs. Vieweg+Teubner, 2011. http://dx.doi.org/10.1007/978-3-8348-9829-6_3.

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Hartt, Kenneth. "Mathematical Modeling." In Mathematical Tools for Physicists. Wiley-VCH Verlag GmbH & Co. KGaA, 2006. http://dx.doi.org/10.1002/3527607773.ch8.

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Ganguli, Ranjan, Dipali Thakkar, and Sathyamangalam Ramanarayanan Viswamurthy. "Mathematical Modeling." In Smart Helicopter Rotors. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24768-7_2.

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Helmig, Rainer. "Mathematical modeling." In Multiphase Flow and Transport Processes in the Subsurface. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60763-9_3.

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Frenz, Christopher. "Mathematical Modeling." In Visual Basic and Visual Basic .NET for Scientists and Engineers. Apress, 2002. http://dx.doi.org/10.1007/978-1-4302-1139-6_13.

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Yang, Xin-She. "Mathematical Modeling." In Mathematical Modeling with Multidisciplinary Applications. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118462706.ch2.

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Boukas, El-Kébir, and Fouad M. AL-Sunni. "Mathematical Modeling." In Mechatronic Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22324-2_3.

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Ledder, Glenn. "Mathematical Modeling." In Mathematics for the Life Sciences. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7276-6_2.

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Moura Neto, Francisco Duarte, and Antônio José da Silva Neto. "Mathematical Modeling." In An Introduction to Inverse Problems with Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32557-1_2.

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Radojčić, Dejan, Milan Kalajdžić, and Aleksandar Simić. "Mathematical Modeling." In Power Prediction Modeling of Conventional High-Speed Craft. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30607-6_2.

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Conference papers on the topic "Mathematical modeling"

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Furman, Maksim R., Anton P. Broyko, Ivan K. Khmelnitskiy, Vagarshak M. Aivazyan, and Ekaterina E. Kholodkova. "Mathematical Modeling of IPMC Sensor." In 2024 International Conference on Electrical Engineering and Photonics (EExPolytech). IEEE, 2024. http://dx.doi.org/10.1109/eexpolytech62224.2024.10755645.

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Riyanto, Bambang. "Designing Mathematical Modeling Tasks for Learning Mathematics." In 2nd National Conference on Mathematics Education 2021 (NaCoME 2021). Atlantis Press, 2022. http://dx.doi.org/10.2991/assehr.k.220403.007.

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Riyanto, Bambang. "Designing Mathematical Modeling Tasks for Learning Mathematics." In 2nd National Conference on Mathematics Education 2021 (NaCoME 2021). Atlantis Press, 2022. http://dx.doi.org/10.2991/assehr.k.220403.007.

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Riyanto, Bambang. "Designing Mathematical Modeling Tasks for Learning Mathematics." In 2nd National Conference on Mathematics Education 2021 (NaCoME 2021). Atlantis Press, 2022. http://dx.doi.org/10.2991/assehr.k.220403.007.

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Xiaoyuan, Luo, and Liu Jun. "Review of Mathematical Modeling in Applied Mathematics Education." In 2013 Fourth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA). IEEE, 2013. http://dx.doi.org/10.1109/isdea.2013.530.

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Ito, H. M. "Introduction to mathematical modeling of earthquakes." In Modeling complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1386820.

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Leite, Lourenildo W. B., Boris P. Sibiryakov, and Wildney W. S. Vieira. "Mathematical Modeling Anticline Reservoirs." In 14th International Congress of the Brazilian Geophysical Society & EXPOGEF, Rio de Janeiro, Brazil, 3-6 August 2015. Brazilian Geophysical Society, 2015. http://dx.doi.org/10.1190/sbgf2015-003.

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Andreotti, Luciano C., and Sérgio N. Vannucci. "Shock Absorber Mathematical Modeling." In SAE Brasil 98 VII International Mobility Technology Conference and Exhibit. SAE International, 1998. http://dx.doi.org/10.4271/982959.

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Abgaryan, Karine. "MATHEMATICAL MODELING OF NEUROMORPHIC SYSTEM." In Mathematical modeling in materials science of electronic component. LLC MAKS Press, 2020. http://dx.doi.org/10.29003/m1518.mmmsec-2020/56-60.

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The paper deals with the creation of mathematical models for the development and optimization of the operation of neuromorphic systems. A multiscale approach based on set-theoretic representations is presented, which makes it possible to quickly develop software with a parallel computing mechanism for creating neuromorphic systems

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Sun, Jie, and Shanshan Wang. "Integration of Mathematical Modeling Thought and Main Courses of Mathematics." In CIPAE 2021: 2021 2nd International Conference on Computers, Information Processing and Advanced Education. ACM, 2021. http://dx.doi.org/10.1145/3456887.3457502.

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Reports on the topic "Mathematical modeling"

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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

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Mitler, Henri E. Mathematical modeling of enclosure fires. National Institute of Standards and Technology, 1991. http://dx.doi.org/10.6028/nist.ir.90-4294.

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Rajagopal, K., M. Massoudi, and J. Ekmann. Mathematical modeling of fluid-solid mixtures. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/7230272.

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Quiang, Ji. Mathematical modeling plasma transport in tokamaks. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/565310.

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Mitler, Henri E., and John A. Rockett. How accurate is mathematical fire modeling? National Bureau of Standards, 1986. http://dx.doi.org/10.6028/nbs.ir.86-3459.

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Jin, D., R. G. Stachowiak, I. V. Samarasekera, and J. K. Brimacombe. Mathematical modeling of deformation during hot rolling. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/34420.

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Lerman, Kristina, Maja Mataric, and Aram Galstyan. Mathematical Modeling of Large Multi-Agent Systems. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada439172.

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Rehm, Ronald G., and Randall J. McDermott. Mathematical modeling of wildland-urban interface fires. National Institute of Standards and Technology, 2011. http://dx.doi.org/10.6028/nist.ir.7803.

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Bras, R. L., G. E. Tucker, and V. Teles. Six Myths About Mathematical Modeling in Geomorphology. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada416086.

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Booth, Victoria, Daniel Forger, and Cecilia Diniz Behn. Mathematical Modeling of Circadian and Homeostatic Interaction. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada563698.

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