ISSN: XXXX-XXXX

Optimizing Numerical Methods for Complex Scientific Models

Abstract

This study integrates results on optimization techniques for complex numerical methods used to analyze complex scientific models. The goal is to improve structural analysis, probabilistic modeling, dynamic systems simulation, integration of multiphysical behaviors, and biological modeling. While optimizing numerical techniques is crucial for the advancement of scientific modeling applications, over-reliance on historical data may neglect emerging trends and lack of accessible data for breakthroughs in new fields. Future research should expand the scope of numerical methods investigated and analyze their effects under different conditions to further explore optimization dynamics. This will fill the gaps in these areas and improve strategies to meet the changing demands of scientific modeling, thereby enhancing the practical applications of numerical methods in various fields.

References

  1. Smith, J., & Patel, R. (2020). The role of machine learning in the interpretation of genomic data. Journal of Computational Biology, 28(3), 159-175.
  2. Garcia, M., & Huang, L. (2021). AI-driven models in astrophysical pattern recognition: A new frontier. Astrophysical Journal, 67(4), 234-248.
  3. Narendra Kumar, B. Srinivas and Alok Kumar Aggrawal: “Web Application Vulnerability Assessment” International Journal of Enterprise computing and Business Systems”, vol-1, 2011(https://www.atlantispress.com/proceedings/cac2s-13/6377)
  4. Megha Singla, Mohit Dua and Narendra Kumar: “CNS using restricted space algorithms for finding a shortest path”. International Journal of Engineering Trends and Technology, 2(1), 48-54, 2011.( https://ijettjournal.org/archive/ijett-v2i1p204)
  5. Narendra Kumar and Anil Kumar “Performance for Mathematical Model of DNA Supercoil.” In the BioScience Research Bulletin, vol 22(2), pp79-87, 2007.( GALE/A199539280)
  6. Roberts, A., & Chang, P. (2019). Optimizing environmental predictions through machine learning models. Environmental Data Science, 12(1), 98-112.
  7. Zhang, H., & Kim, Y. (2022). Handling large datasets with machine learning: Challenges and solutions. Journal of Big Data Analytics, 35(2), 45-59.
  8. Lee, C., & Kim, J. (2023). AI and traditional scientific methods: Bridging the gap. Science Advances, 49(7), 191-202.
  9. Wang, S., & Zhang, W. (2018). Machine learning for big data analytics: An overview. Journal of Data Science and Computing, 40(5), 210-225.
  10. Anuj Kumar, Narendra Kumar and Alok Aggrawal: “An Analytical Study for Security and Power Control in MANET” International Journal of Engineering Trends and Technology, Vol 4(2), 105-107, 2013.
  11. Anuj Kumar, Narendra Kumar and Alok Aggrawal: “Balancing Exploration and Exploitation using Search Mining Techniques” in IJETT, 3(2), 158-160, 2012
  12. Anuj Kumar, Shilpi Srivastav, Narendra Kumar and Alok Agarwal “Dynamic Frequency Hopping: A Major Boon towards Performance Improvisation of a GSM Mobile Network” International Journal of Computer Trends and Technology, vol 3(5) pp 677-684, 2012
  13. Williams, T., & Adams, F. (2020). The effectiveness of machine learning algorithms in scientific computing. Computational Science and Engineering Review, 54(3), 187-205.
  14. Yates, D., & Smith, P. (2021). From data to insights: Machine learning’s role in advancing scientific research. Scientific Computing Journal, 19(2), 78-93.
  15. Davis, J., & Thompson, A. (2023). Challenges in integrating AI with traditional scientific methods. Technology and Science Journal, 29(8), 112-125.
  16. Kumar, R., & Rao, V. (2022). AI and machine learning in genomic research: A comprehensive review. Genomics and Bioinformatics, 39(6), 502-519.
  17. Johnson, D., & Lee, M. (2020). AI applications in genomic data interpretation: Trends and future directions. Bioinformatics, 37(4), 456-468.
  18. Zhang, Q., & Liu, H. (2021). Artificial intelligence in astrophysics: A review of the state-of-the-art techniques. Journal of Astrophysical Research, 48(2), 105-120.
  19. Miller, T., & Cooper, R. (2020). Machine learning approaches to environmental data analysis and prediction. Environmental Modelling & Software, 131, 24-39.
  20. Anderson, E., & Bell, J. (2019). Machine learning for large-scale scientific datasets: A comparative study. Journal of Big Data, 10(3), 48-64.
  21. Roberts, D., & Howard, L. (2022). Impact of machine learning on scientific research: A multi-domain study. Journal of Scientific Computing, 34(7), 115-130.
  22. White, A., & Gonzalez, A. (2023). AI and its applications in environmental science and prediction. Environmental Research Letters, 47(5), 332-347.
  23. Hall, T., & Patel, S. (2021). AI-driven predictive models in environmental science: An overview. Environmental Intelligence, 14(6), 205-219.
  24. Kumar, J., & Srinivasan, S. (2020). The intersection of AI and environmental prediction models: Challenges and advancements. Environmental Computing, 23(1), 31-45.
  25. Chen, M., & Zhang, L. (2022). AI integration with traditional scientific methods: Case studies and lessons learned. Journal of Interdisciplinary Science, 36(4), 123-138.
  26. Fisher, G., & Wang, T. (2019). AI and machine learning applications in scientific computing: A future perspective. Computational Intelligence, 19(8), 88-101.
Download PDF

How to Cite

Krishan kumar Yadav, Dalia Mohamed Younis, (2025-01-07 11:45:50.741). Optimizing Numerical Methods for Complex Scientific Models. Abhi International Journal of Scientific Computing, Volume vZH6gXCrmWsFuEzJN3hb, Issue 1.