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Minor in Scientific Computing

Scientific Computing is the collection of tools, techniques, and theories required to solve, on a computer, mathematical models of problems in Science and Engineering.  Scientific Computing focuses on providing a fully interpretable generative process (model) based on physical laws which govern the system under study.  Modeling and simulation can be used to verify hypotheses, make predictions, and drive experimental data collection. This 15-credit minor will provide students with (i) a solid programming background for running computer simulations, (ii) a computing and modeling course for learning how to develop mathematical models, (iii) a foundational methods course for learning how to analyze the stability and accuracy of computer simulations, and (iv) an elective course to explore additional topics or applications of scientific computing.

Please contact Dr. Vickie Woodard (vweber1@nd.edu) with questions regarding the minor.

The required courses for this minor are as follows:

15 credit hours total

  • 3 credits from First Course in Scientific Computing list
  • 3 credits from Second Course in Scientific Computing list
  • 3 credits from Foundational Methods list
  • 3 credits from Computing and Modeling list
  • 3 credits arising from an additional elective chosen from either Second Course in Scientific Computing, Foundational Methods, Computing and Modeling, or Additional Electives lists

First Course in Scientific Computing

  • Scientific Computing* (ACMS 20210)
  • Scientific Computing with Python* (ACMS 20220)
  • Engineering Computing (AME 2XXXX)
  • Data and Computing for Chemical Engineers (CBE 20258)
  • Fundamentals of Computing* (CSE 20311)
  • Computational Methods in Physics (PHYS 20420)

     * lab component must be taken concurrently

Second Course in Scientific Computing

  • Scientific Programming (ACMS 40210)
  • Advanced Scientific Computing (ACMS 40212)

Foundational Methods

  • Intro Numerical Analysis (ACMS 20350)
  • Numerical Analysis (ACMS/MATH 40390)
  • Numerical Linear Algebra (ACMS 40395)
  • Applied Complex Analysis (ACMS 40485/50485)
  • Nonlinear and Stochastic Optimization (ACMS/CBE 40499)
  • Computational Methods (AME/CE 30125)
  • Chemical Process Control (CBE 30338)

Computing and Modeling

  • Finite Element Methods (ACMS/AME 40541)
  • Nonlinear Dynamical Systems (ACMS 40630)
  • Artificial Neural Networks (ACMS 40640)
  • Mathematical/Comp Modeling (ACMS 40730)
  • Mathematical/Comp Modeling in Neuroscience (ACMS 40740)
  • Introduction to Stochastic Modeling (ACMS 40760)
  • Stochastic Simulation Algorithms (ACMS 40770)
  • Computational Statistics (ACMS 40878)
  • Computational Fluid Dynamics (AME 40532)
  • Introduction to Biocomputing (BIOS 30318)
  • Epidemiology and Ecology of Infectious Diseases (BIOS 40427)
  • Molecular Modeling & Simulation (CBE 40475)
  • Machine Learning for Chemical Engineers (CBE 40501)
  • Computational Chemistry I (CHEM 40650)
  • Intro to Dyn Sys for Scientists (MATH 20480)

Additional Electives

  • Mathematical Cryptography with Python (ACMS 40100)
  • Partial Differential Equations (ACMS/MATH 40750)
  • Bayesian Statistics & Biological Forecasting (BIOS 40552)
  • Process Operations (CBE 40455)

Students are advised to discuss double counting a course with their department/advisor.