Research Article
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Ontology of Stochastic Differential Equations

Year 2025, Volume: 20 Issue: 1, 41 - 53
https://doi.org/10.55525/tjst.1561165

Abstract

This study provides a comprehensive examination of the mathematical formulations, ontological foundations, and application domains of stochastic differential equations (SDEs). SDEs play a critical role in modeling complex phenomena such as uncertainty and randomness and can be applied across a wide range of fields from financial markets to biological systems. The paper contrasts the mathematical approaches of Itô and Stratonovich calculus, detailing the solution methods and theoretical foundations of SDEs. Additionally, the ontological foundations of SDEs and their applications in various scientific and engineering fields are explored. Emphasis is placed on their use in finance, biology, cryptology, and blockchain technology. The results highlight the significance of SDEs in mathematical modeling and their impact across numerous application areas.

Supporting Institution

TÜBİTAK

Project Number

223N142

References

  • Ito K. On Stochastic Differential Equations. Mem Amer Math Soc, 1951; 4: 1-51.
  • Stratonovich RL. A New Representation for Stochastic Integrals and Equations. SIAM Journal on Control, 1966; 4: 362-371.
  • Karatzas SE, Shreve SE. Brownian Motion and Stochastic Calculus. 2nd Ed. USA: Springer-Verlag; 1991.
  • Allen LJS. An Introduction to Stochastic Processes with Applications to Biology, USA: Springer; 2008.
  • Itô K, McKean HP. Diffusion Processes and their Sample Paths, USA: Springer; 1965.
  • Rivest RL, Adleman L, Deaouzos ML. On Data Banks and Privacy Homomorphism. In: DeMillo, RA, editors. Foundations of Secure Computation, USA: Academic Press, 1978: 169-179.
  • Nakamoto S. Bitcoin: A Peer-to-Peer Electronic Cash System; 2008. https://bitcoin.org/bitcoin.pdf
  • Kang H, Chang X, Mišić J, Mišić VB, Yao Y, Chen Z. Stochastic modeling approaches for analyzing blockchain: A survey. arXiv preprint arXiv:2009.05945, 2020.
  • Black F, Scholes M. The pricing of options and corporate liabilities. Journal of Political Economy, 1973; 81(3): 637-654.
  • Frigg R, Hartmann S. Models and Fiction. In The Philosophy of Model-Based Science, USA: Springer, 2012; 49-97.
  • Gardiner CW. Handbook of Stochastic Methods, USA: Springer, 2004.
  • Hull JC. Options, Futures, and Other Derivatives, AUSTRALIA: Pearson 2015.
  • Van Kampen NG. Stochastic differential equations, Physics Reports, 1976; 24(3): 171-228.
  • Merton RC. Theory of rational option pricing. Bell J Econ Manage Sci, 1973; 4(1): 141-183.
  • Nelkin D. Stochastic Processes and Applications. USA: Academic Press, 1983.
  • Norton JD. Mathematical Models in the Sciences: The Role of Simulation anda Computation. MIT Press, 2019.
  • Øksendal B. Stochastic Differential Equations, USA: Springer-Verlag, 2000.
  • Kuznetsov DF. Mean-Square Approximation of Iterated Itˆo and Stratonovich Stochastic Integrals: Method of Generalized Multiple Fourier Series. Application to Numerical Integration of Itˆo SDEs and Semilinear SPDEs. Differential Equations and Control Processes, 2021; 4: 4-14.

Stokastik Diferansiyel Denklemlerin Ontolojisi

Year 2025, Volume: 20 Issue: 1, 41 - 53
https://doi.org/10.55525/tjst.1561165

Abstract

Bu çalışma, stokastik diferansiyel denklemlerin (SDE’ler) matematiksel formülasyonları, ontolojik temelleri ve uygulama alanlarının kapsamlı bir incelemesini sunmaktadır. SDE’ler, belirsizlik ve rastgelelik gibi karmaşık olguların modellenmesinde kritik bir rol oynar ve finansal piyasalardan biyolojik sistemlere kadar çok çeşitli alanlarda uygulanabilir. Makale, Itô ve Stratonovich hesabının matematiksel yaklaşımlarını karşılaştırarak SDE’lerin çözüm yöntemlerini ve teorik temellerini ayrıntılı olarak açıklamaktadır. Ek olarak, SDE’lerin ontolojik temelleri ve çeşitli bilimsel ve mühendislik alanlarındaki uygulamaları incelenmektedir. Finans, biyoloji, kriptoloji ve blok zinciri teknolojisindeki kullanımlarına özel vurgu yapılmaktadır. Sonuçlar, SDE’lerin matematiksel modellemedeki önemini ve çok sayıda uygulama alanındaki etkilerini vurgulamaktadır.

Project Number

223N142

References

  • Ito K. On Stochastic Differential Equations. Mem Amer Math Soc, 1951; 4: 1-51.
  • Stratonovich RL. A New Representation for Stochastic Integrals and Equations. SIAM Journal on Control, 1966; 4: 362-371.
  • Karatzas SE, Shreve SE. Brownian Motion and Stochastic Calculus. 2nd Ed. USA: Springer-Verlag; 1991.
  • Allen LJS. An Introduction to Stochastic Processes with Applications to Biology, USA: Springer; 2008.
  • Itô K, McKean HP. Diffusion Processes and their Sample Paths, USA: Springer; 1965.
  • Rivest RL, Adleman L, Deaouzos ML. On Data Banks and Privacy Homomorphism. In: DeMillo, RA, editors. Foundations of Secure Computation, USA: Academic Press, 1978: 169-179.
  • Nakamoto S. Bitcoin: A Peer-to-Peer Electronic Cash System; 2008. https://bitcoin.org/bitcoin.pdf
  • Kang H, Chang X, Mišić J, Mišić VB, Yao Y, Chen Z. Stochastic modeling approaches for analyzing blockchain: A survey. arXiv preprint arXiv:2009.05945, 2020.
  • Black F, Scholes M. The pricing of options and corporate liabilities. Journal of Political Economy, 1973; 81(3): 637-654.
  • Frigg R, Hartmann S. Models and Fiction. In The Philosophy of Model-Based Science, USA: Springer, 2012; 49-97.
  • Gardiner CW. Handbook of Stochastic Methods, USA: Springer, 2004.
  • Hull JC. Options, Futures, and Other Derivatives, AUSTRALIA: Pearson 2015.
  • Van Kampen NG. Stochastic differential equations, Physics Reports, 1976; 24(3): 171-228.
  • Merton RC. Theory of rational option pricing. Bell J Econ Manage Sci, 1973; 4(1): 141-183.
  • Nelkin D. Stochastic Processes and Applications. USA: Academic Press, 1983.
  • Norton JD. Mathematical Models in the Sciences: The Role of Simulation anda Computation. MIT Press, 2019.
  • Øksendal B. Stochastic Differential Equations, USA: Springer-Verlag, 2000.
  • Kuznetsov DF. Mean-Square Approximation of Iterated Itˆo and Stratonovich Stochastic Integrals: Method of Generalized Multiple Fourier Series. Application to Numerical Integration of Itˆo SDEs and Semilinear SPDEs. Differential Equations and Control Processes, 2021; 4: 4-14.
There are 18 citations in total.

Details

Primary Language English
Subjects Information Modelling, Management and Ontologies, Numerical Solution of Differential and Integral Equations, Applied Mathematics (Other)
Journal Section TJST
Authors

Muharrem Tuncay Gençoğlu 0000-0002-8784-9634

Project Number 223N142
Publication Date
Submission Date October 4, 2024
Acceptance Date November 16, 2024
Published in Issue Year 2025 Volume: 20 Issue: 1

Cite

APA Gençoğlu, M. T. (n.d.). Ontology of Stochastic Differential Equations. Turkish Journal of Science and Technology, 20(1), 41-53. https://doi.org/10.55525/tjst.1561165
AMA Gençoğlu MT. Ontology of Stochastic Differential Equations. TJST. 20(1):41-53. doi:10.55525/tjst.1561165
Chicago Gençoğlu, Muharrem Tuncay. “Ontology of Stochastic Differential Equations”. Turkish Journal of Science and Technology 20, no. 1 n.d.: 41-53. https://doi.org/10.55525/tjst.1561165.
EndNote Gençoğlu MT Ontology of Stochastic Differential Equations. Turkish Journal of Science and Technology 20 1 41–53.
IEEE M. T. Gençoğlu, “Ontology of Stochastic Differential Equations”, TJST, vol. 20, no. 1, pp. 41–53, doi: 10.55525/tjst.1561165.
ISNAD Gençoğlu, Muharrem Tuncay. “Ontology of Stochastic Differential Equations”. Turkish Journal of Science and Technology 20/1 (n.d.), 41-53. https://doi.org/10.55525/tjst.1561165.
JAMA Gençoğlu MT. Ontology of Stochastic Differential Equations. TJST.;20:41–53.
MLA Gençoğlu, Muharrem Tuncay. “Ontology of Stochastic Differential Equations”. Turkish Journal of Science and Technology, vol. 20, no. 1, pp. 41-53, doi:10.55525/tjst.1561165.
Vancouver Gençoğlu MT. Ontology of Stochastic Differential Equations. TJST. 20(1):41-53.