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Computational Thinking: Modeling and Simulation
Design and optimize algorithms to model and simulate real-world physical systems through mathematical abstractions and computational methods.
Design and optimize algorithms to model and simulate real-world physical systems through mathematical abstractions and computational methods.
Develop essential computational thinking skills for modeling and simulating physical systems with this MIT course. Learn to formulate problems for effective computer-based solutions, focusing on representing physical world models. Explore key concepts like representation, decomposition, discretization, and verification in the context of engineering and scientific modeling. Gain practical skills in interpolation, numerical integration and differentiation, and solving linear and non-linear systems of equations. This course bridges mathematics and computer science, emphasizing algorithmic approaches to solving complex problems in engineering and science.
Instructors:
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What you'll learn
Implement and analyze interpolation methods for building simple surrogates of complex functions
Apply numerical integration techniques to solve complex mathematical problems
Develop programs for numerical differentiation in various modeling scenarios
Create algorithms to solve both linear and non-linear systems of equations
Understand and apply concepts of representation and discretization in computational modeling
Evaluate and verify the accuracy and reliability of computational models
Skills you'll gain
This course includes:
Live video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 6 modules in this course
This course focuses on developing computational thinking skills for modeling and simulating physical systems. It covers key concepts such as representation, decomposition, discretization, and verification in the context of creating computer-based models. The curriculum emphasizes algorithmic approaches to mathematical problems, bridging the gap between traditional mathematics and computational methods. Topics include interpolation techniques, numerical integration and differentiation, randomness in modeling, and solving both linear and non-linear systems of equations. Students will learn to select and implement appropriate methods for various modeling tasks, understand their implications for model accuracy, and develop critical verification skills. The course aims to equip learners with the ability to use computers to expand their own thinking and problem-solving capabilities in engineering and scientific contexts.
What is Computational Thinking?
Module 1
Interpolation
Module 2
Integration
Module 3
Randomness
Module 4
Differentiation
Module 5
Solving equations
Module 6
Fee Structure
Instructors
Pioneer in Robust Design and Engineering Education Innovation
Dr. Daniel Frey is a Professor of Mechanical Engineering at MIT and Faculty Research Director of D-Lab, where he leads groundbreaking research in robust design and statistics. After earning his B.S. in Aeronautical Engineering from Rensselaer Polytechnic Institute, M.S. from the University of Colorado, and Ph.D. from MIT, he has established himself as a leader in engineering design methodology. His research focuses on ensuring engineering systems function despite variations in manufacturing, wear, and environmental conditions through innovative statistical approaches. As Head of the Design and Manufacturing Area within Mechanical Engineering and former co-Director of the International Design Center at Singapore University of Technology and Design, he has shaped engineering education globally. His diverse experience includes designing prosthetic devices, flying aircraft in the U.S. Navy, and directing children's television content. His excellence has been recognized through multiple awards, including two R&D 100 Awards, the MIT Baker Award for Outstanding Teaching, and the MIT Aeronautics and Astronautics Teaching Award. Currently, he leads cross-disciplinary research at D-Lab and serves as Principal Investigator for the Comprehensive Initiative on Technology Evaluation, focusing on engineering solutions for developing countries.
Pioneering Roboticist and Computer Vision Expert
Dr. Ali Talebinejad is a distinguished researcher and educator at MIT, combining expertise in robotics, computer vision, and mechanical engineering. After earning his M.S. in Mechanical Engineering focusing on System Dynamics and Control and Ph.D. from MIT's Artificial Intelligence Laboratory in Robotics and Computer Vision, he conducted groundbreaking postdoctoral research on tracking moving objects using video images at the Canadian Institute for Robotics and Intelligent Systems. His industrial experience includes significant contributions at Parametric Technology Corporation, where he worked on Pro Engineer, a leading CAD/CAM software suite. His teaching portfolio spans diverse areas including design, manufacturing, numerical computation, system dynamics, control, robotics, computer vision, and computer programming. In 2018, he reached over 10,000 students through MIT's edX program, teaching "Computational Thinking for Modelling and Simulation." Beyond academia, he is a private pilot and maintains professional memberships in both the American Society of Mechanical Engineers and Institute of Electrical and Electronics Engineers, demonstrating his commitment to both theoretical and practical aspects of engineering
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