Description
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.


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.