APPLICATION OF MATHEMATICS TO OTHER FIELDS
Ключевые слова:
Key words: Mathematics, interdisciplinary, engineering, finance, computer science, modeling, analysis, optimization, algorithms, cryptography, machine learning, cross-disciplinary.Аннотация
Abstract. This article explores the pervasive influence of mathematics across various fields, highlighting its essential role in shaping our understanding and solving complex problems. From engineering to finance and computer science, mathematics provides the foundation for modeling, analysis, and optimization. Through examples ranging from structural engineering to option pricing and machine learning, this abstract showcases mathematics as a unifying language that transcends disciplinary boundaries, driving innovation and progress in diverse domains.
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REFERENCES
Phillips-Wren G, Adya M. Decision making under stress: The role of information overload, time pressure, complexity, and uncertainty. Journal of decision systems. 2020. academia.edu
Pisano R, Sozzo S. A unified theory of human judgements and decision-making under uncertainty. Entropy. 2020. mdpi.com
Rozgonjuk D, Kraav T, Mikkor K, Orav-Puurand K, Täht K. Mathematics anxiety among STEM and social sciences students: the roles of mathematics self-efficacy, and deep and surface approach to learning. International Journal of STEM Education. 2020 Dec;7:1-1. springer.com
Kumar S, Jain A, Shukla AP, Singh S, Raja R, Rani S, Harshitha G, AlZain MA, Masud M. A comparative analysis of machine learning algorithms for detection of organic and nonorganic cotton diseases. Mathematical Problems in Engineering. 2021 Jun 16;2021:1-8. hindawi.com
Karniadakis GE, Kevrekidis IG, Lu L, Perdikaris P, Wang S, Yang L. Physics-informed machine learning. Nature Reviews Physics. 2021 Jun;3(6):422-40. osti.gov
Stewart, James. "Calculus: Concepts and Contexts." Cengage Learning, 2015.
Kreyszig, Erwin. "Advanced Engineering Mathematics." John Wiley & Sons, 2018.
Strang, Gilbert. "Introduction to Linear Algebra." Wellesley-Cambridge Press, 2016.
Reddy, J. N. "An Introduction to the Finite Element Method." McGraw-Hill Education, 2018.
Boyd, Stephen, and Vandenberghe, Lieven. "Convex Optimization." Cambridge University Press, 2004.
Goldberg, David E. "Genetic Algorithms in Search, Optimization, and Machine Learning." Addison-Wesley, 1989.
Hull, John C. "Options, Futures, and Other Derivatives." Pearson, 2017.
Tsay, Ruey S. "Analysis of Financial Time Series." John Wiley & Sons, 2010.
Wilmott, Paul. "Paul Wilmott Introduces Quantitative Finance." John Wiley & Sons, 2007.
Shreve, Steven E. "Stochastic Calculus for Finance II: Continuous-Time Models." Springer, 2008.
Jorion, Philippe. "Value at Risk: The New Benchmark for Managing Financial Risk." McGraw-Hill Education, 2006.
McNeil, Alexander J., et al. "Quantitative Risk Management: Concepts, Techniques and Tools." Princeton University Press, 2015.
Cormen, Thomas H., et al. "Introduction to Algorithms." MIT Press, 2009.
Sedgewick, Robert, and Wayne, Kevin. "Algorithms." Addison-Wesley, 2011.
Katz, Jonathan, and Lindell, Yehuda. "Introduction to Modern Cryptography." CRC Press, 2014.
Paar, Christof, and Pelzl, Jan. "Understanding Cryptography: A Textbook for Students and Practitioners." Springer, 2010.
Diestel, Reinhard. "Graph Theory." Springer, 2017.
Newman, Mark. "Networks: An Introduction." Oxford University Press, 2018.
Bishop, Christopher M. "Pattern Recognition and Machine Learning." Springer, 2006.
Hastie, Trevor, et al. "The Elements of Statistical Learning: Data Mining, Inference, and Prediction." Springer, 2009.
