Catholic Tech

Computation and Modeling

Course Topics

  1. Week 1: Introduction to Computational Modeling
    • Introduction to computational modeling and its importance in scientific and engineering disciplines.
    • Overview of numerical methods and their applications.
    • Introduction to programming languages commonly used in scientific computing.
  2. Week 2: Mathematical Preliminaries
    • Review of calculus and linear algebra concepts relevant to computational modeling.
    • Introduction to numerical differentiation and integration.
  3. Week 3: Introduction to Numerical Methods
    • Interpolation and approximation techniques.
    • Root-finding algorithms.
    • Numerical integration methods.
  4. Week 4: Ordinary Differential Equations (ODEs)
    • Introduction to ODEs and their applications.
    • Numerical methods for solving ODEs, including Euler's method and Runge-Kutta methods.
  5. Week 5: Partial Differential Equations (PDEs)
    • Introduction to PDEs and their applications.
    • Finite difference methods for solving PDEs.
    • Introduction to finite element methods.
  6. Week 6: Statistical Modeling and Simulation
    • Probability and statistics concepts relevant to modeling and simulation.
    • Monte Carlo simulation methods.
    • Applications of statistical modeling and simulation in engineering and science.
  7. Week 7: Optimization and Parameter Estimation
    • Introduction to optimization techniques.
    • Parameter estimation methods and their applications in modeling.
    • Applications of optimization and parameter estimation in real-world problems.
  8. Week 8: Agent-Based Modeling and Simulation
    • Introduction to agent-based modeling.
    • Simulation of complex systems using agent-based models.
    • Applications of agent-based modeling in various domains.
  9. Week 9: Data Analysis and Visualization
    • Techniques for analyzing and visualizing computational and experimental data.
    • Introduction to data visualization libraries and tools.
    • Case studies of data analysis and visualization in scientific and engineering contexts.
  10. Week 10: Introduction to Machine Learning
    • Overview of machine learning concepts and algorithms.
    • Applications of machine learning in computational modeling.
    • Introduction to popular machine learning libraries and frameworks.
  11. Week 11: High-Performance Computing
    • Introduction to parallel computing and distributed computing.
    • Techniques for improving computational efficiency.
    • Introduction to parallel computing frameworks.
  12. Week 12: Case Studies and Applications
    • Exploration of case studies and applications of computational modeling in various disciplines.
    • Guest lectures from experts in specific domains.
  13. Week 13: Project Work
    • Independent or group projects to apply computational modeling techniques to a real-world problem.
    • Project proposal development and feedback sessions.
  14. Week 14: Project Presentations and Course Conclusion
    • Final project presentations.
    • Course summary and review.
    • Discussion on future trends and advancements in computational modeling.


The course syllabus is subject to change at the discretion of the instructor. Any modifications or updates will be communicated in advance.