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Computational Science and Engineering
Master advanced problem-solving in engineering through Python programming, numerical methods, and computational analysis techniques.
Master advanced problem-solving in engineering through Python programming, numerical methods, and computational analysis techniques.
Master the fundamentals of computational science and engineering with this comprehensive MIT course. Learn to use Python programming to solve real-world engineering and scientific problems, including simulations of time-dependent phenomena, system optimization, and uncertainty quantification. Through hands-on projects, you'll simulate Mars lander descent, optimize cellular tower placement, and assess climate change scenarios. Gain proficiency in advanced Python programming, numerical methods, and data visualization techniques essential for modern engineering and scientific computing.
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What you'll learn
Master advanced Python programming techniques using Python 3 and NumPy
Develop skills in data visualization using Matplotlib
Apply numerical methods to solve initial value problems and differential equations
Implement discretization techniques with explicit and implicit methods
Solve linear and nonlinear systems of equations using computational methods
Perform unconstrained optimization using gradient descent algorithms
Skills you'll gain
This course includes:
Live video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course provides a comprehensive introduction to computational science and engineering, focusing on using computing to solve complex problems in engineering and science. Students will learn advanced programming techniques in Python and NumPy, and apply these skills to simulate time-dependent phenomena, optimize systems, and quantify uncertainty. The curriculum covers a wide range of topics including numerical methods for solving initial value problems, discretization techniques, solution of linear and nonlinear systems of equations, unconstrained optimization using gradient descent, and Monte Carlo simulations for probability and statistics. Throughout the course, students will work on practical projects that apply these concepts to real-world scenarios, such as simulating a Mars lander descent, optimizing cellular tower placement, and analyzing climate change probabilities.
Fee Structure
Instructor
Distinguished Aerospace Engineer and Computational Science Pioneer
Dr. David Darmofal is the Jerome C. Hunsaker Professor of Aeronautics and Astronautics at MIT, where he has made significant contributions to computational fluid dynamics and engineering education. After earning his BS from the University of Michigan in 1989 and his SM and Ph.D. from MIT in 1991 and 1993 respectively, he began his academic career at Texas A&M University before joining MIT in 1998. His research focuses on computational methods for partial differential equations and fluid dynamics, with approximately 80 technical publications in peer-reviewed journals and conferences. His excellence in teaching has been recognized through numerous awards, including the MIT School of Engineering Bose Award (2002), MacVicar Faculty Fellowship (2004), and the Earll M. Murman Award for Excellence in Undergraduate Advising (2011). Beyond his academic work, he serves as Head of House at MIT's graduate residence, The Warehouse, with his wife Claudia since 2017. A recipient of the NSF CAREER Award in 1997, he is an Associate Fellow of AIAA and member of ASME, ASEE, and SIAM, demonstrating his broad impact across aerospace engineering and computational science.
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