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Interdisciplinary Approaches in Mathematical Sciences

Meena Iyer*

Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India

*Corresponding Author:
Meena Iyer
Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India
E-mail: meenaiyer@iisc.ac.in

Received: 01-Mar-2025, Manuscript No. JSMS-25-169987; Editor assigned: 4-Mar-2025, Pre-QC No. JSMS-25-169987 (PQ); Reviewed: 20-Mar-2025, QC No JSMS-25-169987; Revised: 26-Mar- 2025, Manuscript No. JSMS-25-169987 (R); Published: 30-Mar-2025, DOI: 10.4172/RRJ Stats Math Sci. 11.01.002

Citation: Meena Iyer, Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India. RRJ Mater Sci. 2025.11.002.

Copyright: © 2025 Meena Iyer, this is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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INTRODUCTION

Mathematics increasingly interacts with other sciences, creating interdisciplinary research that addresses global challenges. This article explores five intersections: bioinformatics, environmental modeling, artificial intelligence, social network analysis, and quantum computation.

Interdisciplinary Contributions

Bioinformatics: Mathematics supports genetic sequencing and protein structure prediction. Algorithms for sequence alignment and probability models help understand biological evolution [1].

Environmental Modeling: Climate change research relies on mathematical models for atmospheric dynamics, carbon cycles, and groundwater systems. PDE-based simulations guide policy decisions [2].

Artificial Intelligence: Mathematics drives AI through optimization, linear algebra, and probability. Reinforcement learning and neural networks are grounded in rigorous mathematical theory [3].

Social Network Analysis: Graph theory and statistics enable mapping of social dynamics, misinformation spread, and influence patterns across digital platforms [4].

Quantum Computation: Quantum algorithms leverage linear algebra and probability to revolutionize computing power, with applications in optimization and cryptography [5].

REFERENCES

  1. Keeling MJ. Modeling Infectious Diseases in Humans and Animals. Princeton University Press. 2008.
  2. Murray JD. Mathematical Biology I: An Introduction. Springer. 2002.
  3. Edelstein-Keshet L. Mathematical Models in Biology. SIAM. 2005.
  4. Allen LJS. An Introduction to Stochastic Processes with Applications to Biology. CRC Press. 2017.
  5. Ferziger JH. Computational Methods for Fluid Dynamics. Springer. 2019.