Course description
Computational Thinking for Modeling and Simulation
Computational thinking is becoming widely recognized as a skill necessary for every educated person in a technologically advanced society.
We will focus on just a subset of computational thinking which concerns creating models of the physical world – something that engineers frequently need to do. Because of that choice, this course covers many topics normally viewed as within the domain of mathematics such as algebra and calculus, but the solution procedures are algorithmic rather than symbolic.
The major themes of the course are:
- Representation -- How do you encode information about the world in a computer? How do your choices in representation affect the ease with which you can solve problems?
- Decomposition -- How do you break a large and diverse problem into many simpler parts?
- Discretization -- How do you break up space and time into a large number of relatively small pieces? What are the alternative ways of doing this? What are the consequences of discretization procedures for accuracy and speed?
- Verification -- How do you build confidence in the results of a model?
Upcoming start dates
Who should attend?
Prerequisites
- Algebra
- Calculus
Training content
- What is Computational Thinking? (representation, discretization, error, decomposition, verification)
- Interpolation (building simple surrogates for more complex functions)
- Integration (processes for numerical quadrature)
- Randomness (generating and using pseudorandom variables in models)
- Differentiation (numerical derivatives)
- Solving equations (Gaussian elimination for linear systems, Newton-Raphson for non-linear systems)
Course delivery details
This course is offered through Massachusetts Institute of Technology, a partner institute of EdX.
3-5 hours per week
Costs
- Verified Track -$49
- Audit Track - Free
Certification / Credits
What you'll learn
By the end of this course, students will be able to:
- Select and implement methods for interpolation and understand their consequences for convergence of model results as discretization is refined.
- Carry out a few simple methods for numerical integration
- Implement procedures for numerical differentiation
- Write programs to solve systems of equations, both linear and non-linear
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