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1
Academic Journal
Data simulation of optimal model for numerical solution of differential equations based on deep learning and genetic algorithm

Authors: Jing, LiAff1, IDs00500023082087_cor1

Superior Title: Soft Computing: A Fusion of Foundations, Methodologies and Applications. :1-8

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2
Video Recording
Solve differential equations in Python.

Contributors: Knowledgemotion Ltd., film distributor., APMonitor.com, publisher.

Availability: https://www.aspresolver.com/aspresolver.asp?MARC;5144348


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4
Academic Journal
Efficient direct sampling type methods for some inverse problems

Contributors: Han, Fuqun (author.), Zou, Jun , 1962- (thesis advisor.), Chinese University of Hong Kong Graduate School. Division of Mathematics. (degree granting institution.)

Subject Terms: Inverse problems (Differential equations)--Numerical solutions, Sampling (Statistics), QA378.5 .H36 2020eb

File Description: electronic resource; remote; 1 online resource (ix, 81 leaves) : illustrations (chiefly color); computer; online resource

Availability: https://julac.hosted.exlibrisgroup.com/primo-explore/search?query=addsrcrid,exact,991040013774503407,AND&tab=default_tab&search_scope=All&vid=CUHK&mode=advanced&lang=en_US
https://repository.lib.cuhk.edu.hk/en/item/cuhk-2628012


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6
Academic Journal
Neutron density calculation using the generalised adams-bashforth-moulton method ; Cálculo da densidade de nêutrons usando o método generalizado de Adams-Bashforth-Moulton ; Cálculo de densidad de neutrones utilizando el método generalizado de Adams-Bashforth-Moulton

Authors: Suescún-Díaz, Daniel, Rasero, Diego, Lozano Parada, Jaime Humberto

Subject Terms: Reacciones nucleares, Ecuaciones diferenciales con retardo - Soluciones numéricas, Nuclear reactions, Delay differential equations - Numerical solutions, Nuclear reactor power, Nuclear density, Point kinetics equations, Numerical methods, Densidade nuclear, Potência do reator nuclear, Métodos numéricos, Equações da cinetica pontual

File Description: 21 páginas; application/pdf

Relation: Volumen 24, número 3 (2019); 563; 543; 24; Suescún Díaz, D., Rasero Causil, D.A., Lozano Parada, J.H. (2019). Neutron Density Calculation Using the Generalised Adams-Bashforth-Moulton Method. Universitas Scientiarum. Pontificia Universidad Javeriana. (Vol. 24 (3), pp. 543-563, 2019. doi:10.11144/Javeriana.SC24-3.ndcu; Universitas Scientiarum; [1] Chao YA, Attard A. A resolution of the stiffness problem of reactor kinetics, Nuclear Science and Engineering, 90(1):40-46, 1985. doi:10.13182/NSE85-A17429; [2] Sánchez J. On the numerical solution of the point reactor kinetics equations by generalized Runge-Kutta methods, Nuclear Science and Engineering, 103: 94-99, 1989. doi:10.13182/NSE89-A23663; [3] Aboanber AE, Nahla AA. Solution of the point kinetics equations in the presence of Newtonian temperature feedback by Padé approximation via the analytical inversion method, Journal of Physics A: Mathematical and General, 35(45):9609-9627, 2002b. doi:10.1088/0305-4470/35/45/309; [4] Aboanber AE, Nahla AA. Generalization of the analytical inverse method for the solution of point kinetics equations, Journal of Physics A: Mathematical and General, 35(14): 3245-3263, 2002a. doi:10.1088/0305-4470/35/14/307; [5] Aboanber AE. Analytical solution of the point kinetics equations by exponential mode analysis, Progress in Nuclear Energy, 42(2): 179-197, 2003. doi:10.1016/s0140-6701(03)82201-4; [6] Kinard, M.; Allen, E. J.: Efficient numerical solution of the point kinetics equations in nuclear reactor dynamics, Annals of Nuclear Energy, 31(9): 1039-1051, 2004. doi:10.1016/j.anucene.2003.12.008; [7] Quintero LB. CORE: a numerical algorithm to solve the point kinetics equations, Annals of Nuclear Energy, 35(11): 2136-2138, 2008. doi:10.1016/j.anucene.2008.07.002; [8] Li H, Chen W, Luo L, Zhu Q. A new integral method for solving the point reactor neutron kinetics equations, Annals of Nuclear Energy, 36(4): 427-432, 2009. doi:10.1016/j.anucene.2008.11.033; [9] Nahla, A. A.: Taylor series method for solving the nonlinear point kinetics equations, Nuclear Engineering and Design, 241(5): 1592-1595, 2011. doi:10.1016/j.nucengdes.2011.02.016; [10] Hamada, Y. M.: Generalized power series method with step size control for neutron kinetics equations, Nuclear Engineering and Design, 241(8): 3032-3041, 2011. doi:10.1016/j.nucengdes.2011.05.006; [11] Hamada YM. Confirmation of accuracy of generalized power series method for the solution of point kinetics equations with feedback, Annals of Nuclear Energy, 55: 184-193, 2013. doi:10.1016/j.anucene.2012.12.013; [12] Ganapol BD. A highly accurate algorithm for the solution of the point kinetics equations, Annals of Nuclear Energy, 62: 564- 571, 2013. doi:10.1016/j.anucene.2012.06.007; [13] Picca P, Furfaro R, Ganapol B. A highly accurate technique for the solution of the non-linear point kinetics equations, Annals of Nuclear Energy, 58: 43-53, 2013. doi:10.1016/j.anucene.2013.03.004; [14] Salah A. Hassan SA. Samia.: The Analytical Algorithm for the Differential Transform Method to Solution of the Reactor Point kinetics Equations, World Applied Sciences Journal, 27(3):367-370, 2013. doi:10.5829/idosi.wasj.2013.27.03.1601; [15] Kim HT, Park Y, Kazantzis N, Parlos A, Vista IV F, Chong KT. A numerical solution to the point kinetic equations using Taylor-Lie series combined with a scaling and squaring technique, Nuclear Engineering and Design, 272: 1-10, 2014. doi:10.1016/j.nucengdes.2013.12.066; [16] Patra A, Ray SS. A numerical approach based on Haar wavelet operational method to solve neutron point kinetics equation involving imposed reactivity insertions, Annals of Nuclear Energy, 68: 112-117, 2014. doi:10.1016/j.anucene.2014.01.008; [17] Leite QB, Palma AP, Vilhena MT, Bodmann EJ. Analytical representation of the solution of the point reactor kinetics equations with adaptive time step, Progress in Nuclear Energy, 70: 112-118, 2014. doi:10.1016/j.pnucene.2013.07.008; [18] Hamada YM. Trigonometric Fourier-series solutions of the point reactor kinetics equations. Nuclear Engineering and Design, 281: 142-153, 2015. doi:10.1016/j.nucengdes.2014.11.017; [19] Razak MA, Devan K, Sathiyasheela T. The modified exponential time differencing (ETD) method for solving the reactor point kinetics equations, Annals of Nuclear Energy, 76: 193-199, 2015. doi:10.1016/j.anucene.2014.09.020; [20] Nahla AA. Numerical treatment for the point reactor kinetics equations using theta method, eigenvalues and eigenvectors, Progress in Nuclear Energy, 85: 756-763, 2015. doi:10.1016/j.pnucene.2015.09.008; [21] Suescún DD, Narváez PM, Lozano PH. Calculation of Nuclear Reactivity Using the Generalised Adams Bashforth-Moulton Predictor-Corrector Method, Kerntechnik, 81(1): 86-93, 2016. doi:10.3139/124.110591; [22] Yun C, Xingjie P, Qing L, Kan W. A numerical solution to the nonlinear point kinetics equations using Magnus expansion, Annals of Nuclear Energy, 89: 84-89, 2016. doi:10.1016/j.anucene.2015.11.021; [23] Duderstadt JJ, Hamilton LJ. Nuclear Reactor Analysis, second ed. John Wiley & Sons Inc., New York, 1976; 1227483; https://hdl.handle.net/10614/13432

Availability: https://doi.org/10.11144/Javeriana.SC24-3.ndcu
https://doi.org/10.13182/NSE85-A17429
https://doi.org/10.13182/NSE89-A23663
https://doi.org/10.1088/0305-4470/35/45/309
https://doi.org/10.1088/0305-4470/35/14/307
https://doi.org/10.1016/s0140-6701(03)82201-4
https://doi.org/10.1016/j.anucene.2003.12.008
https://doi.org/10.1016/j.anucene.2008.07.002
https://doi.org/10.1016/j.anucene.2008.11.033
https://doi.org/10.1016/j.nucengdes.2011.02.016


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11
Academic Journal
Numerical methods and their analyses for backward Stochastic differential equations

Contributors: Zhang, Yufei (author.), Zou, Jun , 1962- (thesis advisor.), Chinese University of Hong Kong Graduate School. Division of Mathematics. (degree granting institution.)

Subject Terms: Stochastic differential equations--Numerical solutions, Stochastic processes, QA274.23 .Z63 2017eb

File Description: electronic resource; remote; 1 online resource (82 leaves) : illustrations (some color); computer; online resource

Availability: https://julac.hosted.exlibrisgroup.com/primo-explore/search?query=addsrcrid,exact,991039531367003407,AND&tab=default_tab&search_scope=All&vid=CUHK&mode=advanced&lang=en_US
https://repository.lib.cuhk.edu.hk/en/item/cuhk-1839481


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12
Dissertation/ Thesis
Un modelo matemático de diabetes basado en ecuaciones diferenciales estocásticas ; A mathematical model of diabetes based on stochastic differential equations

Authors: Capera Tovar, Cindy Lorena

Contributors: Ruiz Vera, Jorge Mauricio

Subject Terms: 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas, ECUACIONES DIFERENCIALES ESTOCASTICAS, ECUACIONES DIFERENCIALES-SOLUCIONES NUMERICAS, Stochastic differential equations, Differential equations - numerical solutions, Diabetes, Insulina, Glucosa, Procesos estocásticos, Trayectorias brownianas, Insulin, Glucose, Stochastic processes, Brownian trajectories

File Description: xiv, 105 páginas; application/pdf

Relation: RedCol; LaReferencia; Barceló A. Diabetes in the Americas. Pan American Journal of Public Health 22 (2001).; Cobelli, C. Bettini, F. & Caumo, A. Overestimation of Minimal Model Glucose Effectiveness in Presence of Insulin Response is Due to Undermodeling. American of Physiology-Endocrinology and Metabolism (1998), E1031-E1036.; Oksendal B. Stochastic Differential Equations, An Introduction with Applications. New York: Springer-Verlag Heidelberg, Fifth Edition, 2000.; Hipszer, B.R. A Mathematical Model of Glucose Metabolism in Hospitalized Patients with Diabetes and Stress Hyperglycemia. Pensilvania, Estados Unidos: Drexel University, 2008.; Zamarron, C. Modelos matemáticos del sistema regulador glucosa-insulina en pacientes diabéticos con retraso de tiempo. Tesis de maestría. Madrid: Universidad Pontificia Comillas, 2021.; Bergman, R.N. Ider, Y.Z. & Bowden, C.R. Quantitative estimation of insulin sensitivity. American Journal of Physiology-Endocrinology and Metabolism 236 (1979), págs. 667-677.; Diabetes Education. https://dtc.ucsf.edu/. 2020.; Salud DKV. Glucosa. https://quierocuidarme.dkv.es/salud-para-todos/glucosa-que-es. 2017.; Allen, E. Modeling with Ito Stochastic Differential Equations. Texas Tech University, USA: Springer, Volume 22, 2007.; Platen, E. Numerical solution of stochastic differential equations. New York: Springer, 1992.; Vilhjálmsdóttir, E. Deterministic and Stochastic Modeling of Insulin Sensitivity. Tesis de maestría. Chalmers University of Technology, 2013.; Cuatrecasas, G. Complicaciones crónicas de la diabetes. Barcelona, España, 2018.; Dávila, C.A. Agudelo, M. & Hernández, G. Diabetes en México y Colombia: Análisis de la tendencia de años de vida perdidos, 1998-2007. Revista de salud pública 13 (2011), págs. 560-571.; Glicksman, H. When blood glucose control fails. Evolution news and science (2018).; Cisneros, I. Modelos matemáticos para la diabetes. Tesis de maestría. España: Universidad de Cantabria, 2014.; Kapur, J.N. Mathematical Modelling. New Delhi: New age international publishers, 1998.; Ackerman, E. Gatewood, L.C. & Rosevear, J.W. Model studies of blood-glucose regulation. Boletín of mathematical biophysics 27 (1965), págs. 21-37.; Morton, K.W. Numerical Solution of Partial Differential Equations. New York: Cambridge University Press, 1994.; Blanco, L. Probabilidad. Bogotá D.C.: Universidad Nacional de Colombia, 2004; Braun, M. Ecuaciones diferenciales y sus aplicaciones. México D.F.: Grupo Editorial Iberoamérica, 1990.; Elaine, M. Anatomía y fisiología humana. Madrid, España: Pearson, 2008.; Drucker, D. & Nauck, M.A. The incretin system in type 2 diabetes. Lancet 368 (2006), págs. 1696-1705.; Sarwar, N. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease. Lancet 375 (2010), págs. 215-222.; Organización Mundial de la Salud OMS. Informe mundial sobre la diabetes. www.who.int/diabetes. 2016.; Producción de insulina. https://www2.uned.es/pea- nutricion- y- dieteticaI/guia/enfermedades/diabetes/manual_produccion_de_ins.htm. 2021.; López, R. Métodos numéricos para la solución de ecuaciones diferenciales estocásticas. Tesis de maestría. Universidad Autónoma de Puebla, 2014.; Rupert, R. Causes of vision loss worldwide, 1990-2010: a systematic analysis. Lancet Glob Health 339 (2013), pág. 49.; Saran, R. US Renal Data System 2014 Annual Data Report. Am J Kidney Dis 66 (2015), pág. 545.; Duun, A.K. Schmidt, S. & Roge, R.M. Model identification using stochastic differential equation Grey-Box models in diabetes. Journal of Diabetes Science and Technology 7 (2013), págs. 431-440.; Morales, V. Ampliación de un modelo matemático incluyendo la actividad física para la predicción de los niveles de glucosa en sangre de forma continua en pacientes diabéticos. Tesis de maestría. Universidad Politécnica de Cartagena, 2017.; VectorStock. Human glucose levels hyperglycemia normal vector. https://www.vectorstock.com/royalty-free-vector/human-glucose-levels-hyperglycemia-normal-vector-21042423. 2021.; Zhifang, Z. Zhan, Q. & Xie, X. Numerical Study on Stochastic Diabetes Mellitus Model with Additive Noise. Hindawi (2019), pág. 8.; https://repositorio.unal.edu.co/handle/unal/81776; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

Availability: https://repositorio.unal.edu.co/handle/unal/81776
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