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Introduction to Computational Science and Engineering

Description

CSE.0002x will teach you how to use to solve problems in engineering and science including simulation of time-dependent phenomena; optimization of systems; and quantification of uncertainty. This course is primarily for learners with some prior programming experience in Python and an introductory knowledge of calculus and mechanics (typical of a first-year college course in these topics). You will spend a considerable amount of time writing programs to implement the concepts covered in the course. For example, you will write programs that will: simulate the descent of a lander in the Martian atmosphere; optimize the placement of cellular towers on the MIT campus; and quantify the likelihood of significant climate temperature rise under different scenarios.

Topics covered include:

  • Advanced programming in Python 3 and NumPy
  • Plotting with Matplotlib
  • Initial value problems
  • Discretization with explicit and implicit methods
  • Solution of linear and nonlinear systems of equations
  • Unconstrained optimization and gradient descent
  • Probability, distributions
  • Monte Carlo simulations
  • Confidence intervals

Online Courses

EdX

Free to Audit

9 weeks, 14-16 hours a week

Introduction to Computational Science and Engineering

Affiliate notice

  • Type
    Online Courses
  • Provider
    EdX
  • Pricing
    Free to Audit
  • Duration
    9 weeks, 14-16 hours a week

CSE.0002x will teach you how to use to solve problems in engineering and science including simulation of time-dependent phenomena; optimization of systems; and quantification of uncertainty. This course is primarily for learners with some prior programming experience in Python and an introductory knowledge of calculus and mechanics (typical of a first-year college course in these topics). You will spend a considerable amount of time writing programs to implement the concepts covered in the course. For example, you will write programs that will: simulate the descent of a lander in the Martian atmosphere; optimize the placement of cellular towers on the MIT campus; and quantify the likelihood of significant climate temperature rise under different scenarios.

Topics covered include:

  • Advanced programming in Python 3 and NumPy
  • Plotting with Matplotlib
  • Initial value problems
  • Discretization with explicit and implicit methods
  • Solution of linear and nonlinear systems of equations
  • Unconstrained optimization and gradient descent
  • Probability, distributions
  • Monte Carlo simulations
  • Confidence intervals