Computation for Design and Optimization (CDO)
Intensive Computation for Design and Optimization (CDO) has
become an essential activity in such diverse areas as
telecommunications, imaging, guidance/control, the Internet,
aerospace design, micromachined devices, distribution networks,
traffic management, air transport, web-based retailing, the
electric power grid, and manufacturing scheduling. Effective
computation produces shorter design cycle times, higher-quality
products, and improved functionality.
The MIT CDO program offers a unified treatment of the computational aspects of complex engineered systems. Through hands-on projects and a master's thesis, students develop and apply advanced computational methods to a diverse range of applications, from aerospace to nanotechnology, from Internet protocols to telecommunications system design. Career opportunities for CDO graduates include companies and research centers where systems modeling, numerical simulation, design and optimization play a critical role.
The MIT CDO program educates students in the formulation, analysis, implementation, and application of computational approaches to designing and operating engineered systems, emphasizing:
- Breadth through introductory courses in numerical analysis and simulation, optimization, and applied probability
- Depth in optimization methods and numerical methods for partial differential equations
- Multidisciplinary aspects of computation
- Hands-on experience through projects, assignments, and a
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Working papers relevant to CDO.
(Massachusetts Institute of Technology, 2019)The food safety problem has been challenging the traditional operating model of Chinese agricultural supply chain. In the recent decades, more and more agribusinesses and cooperatives in China have adopted contract farming ...
(Massachusetts Institute of Technology, 2019)Machine-Learning Interatomic Force-Fields have shown great promise in increasing time- and length-scales in atomistic simulations while retaining the high accuracy of the reference calculations that they are trained on. ...
(Massachusetts Institute of Technology, 2018)Solving convex optimization problems has become extremely efficient and reliable after the recent development of polynomial-time algorithms and advancement in computing power. Geometric Programming (GP) and Signomial ...