Summary

Computer simulation is heavily used in science and engineering as a tool in analysis, visualization, and design. Complex mathematical models can give very accurate predictions of real-world phenomena, but typically lead to equations that can only be solved with the aid of a computer. The Scientific Computing and Numerical Algorithms track focuses on the design, analysis and efficient implementation of numerical algorithms for such problems.  Such a background will prepare the students well for either an industry job or graduate school in various interdisciplinary programs that focus on scientific computing and its applications.

Learning Objectives

  1. Design numerical algorithms for simulating problems in science and engineering.
  2. Analyze various aspects of computer simulation methods (e.g., efficiency, accuracy).
  3. Implement numerical algorithms using standard software tools.
  4. Identify appropriate applications for high performance computing.

 

ACMS Program Core (28-29 credits)


Option Core (45-46 credits)

  • Probability (one of):
    • STAT 390: (4) Probability and Statistics or
    • MATH/STAT: 394 (3) Probability I
  • Modeling (one of):
    • AMATH 383: (3) Continuous Math Modeling or
    • Math 381: (3) Discrete Math Modeling
  • Calculus Based Science (5 credits)
    • PHYS 121**: (5) Mechanics
  • Other Science or Engineering (1 of the following courses, 5 credits)
    • PHYS 122/123: (5,5) Electromagnetism; Waves, Light, and Heat or
    • CHEM 142/152: (5,5) General Chemistry or
    • BIOL 180: (5) Introductory Biology
  • Tools for Scientific Computing (8 credits)
    • CSE 163: (4) Intermediate Data Programming
    • AMATH 301**: (4) Beginning Scientific Computing
  • Numerical Analysis (6 credits)
    • MATH 464: (3) Numerical Analysis I
    • MATH 465: (3) Numerical Analysis II
  • Scientific Computing (15 credits)
    • AMATH 481: (5) Scientific Computing
    • AMATH 482: (5) Computational Methods for Data Analysis
    • AMATH 483: (5) High-Performance Scientific Computing

Electives (10 credits)

Any course listed above can be an elective, unless it is used towards a requirement. Can also choose from the pre-approved list below.

  • AMATH 353: (3) Partial Differential Equations & Fourier Analysis
  • AMATH 401/402: (4,4) Methods of Applied Mathematics I & II
  • AMATH 403: (4) Methods of Applied Mathematics III
  • AMATH 422/423 (3,3): Mathematical Biology
  • CSE 373: (4) Data Structures
  • CSE 410: (3) Computer Systems
  • CSE 412: (3) Data Visualization
  • CSE 417: (3) Algorithms and Complexity
  • MATH 407/408/409: (3, 3, 3) Linear Optimization, Nonlinear Optimization, Discrete Optimization
  • MATH 427/428: (3,3) Complex Analysis
  • MATH 461/462: (3,3) Combinatorial Theory
  • MATH 395: (3) Probability II
  • MATH/STAT 491/492: (3,3) Introduction to Stochastic Processes
  • PHYS 417: (3) Neural Network Methods for Signals in Engineering and Physical Sciences
  • PHYS 434: (3) Advanced Laboratory: Computational Data Analysis
  • STAT 403: (3) Introduction to Resampling Inference

Courses with ** would indicate demonstrated interest in this degree option, if taken prior to applying to the ACMS program.