Center for Computational Science and Engineering (CCSE)
Established in 2008 and incorporated into the Schwarzman College of Computing as one of its core academic units in January 2020, the MIT Center for Computational Science and Engineering (CCSE) is an interdisciplinary research and education center focused on innovative methods and applications of computation. CCSE involves faculty, researchers, and students from MIT’s Schools of Engineering, Science, Architecture and Planning, and Management, as well as other units of the Schwarzman College of Computing. Our educational programs seek to train future generations of computational scientists and engineers to both develop and use sophisticated computational methods for a wide variety of applications.
CCSE oﬀers two graduate programs (SM & PhD) in Computational Science and Engineering (CSE). The CSE master’s degree program is interdisciplinary, providing students with a strong foundation in computational methods for the simulation, design, and analysis of complex engineered and scientific systems; it emphasizes educational breadth through introductory core courses in numerical analysis, simulation, and optimization, and depth though an elective component that focuses on particular applications as well as a thesis. Students in the CSE PhD program undertake advanced specialization in a computation-related ﬁeld of their choice through focused coursework and a doctoral thesis in this ﬁeld. The CSE PhD degree is awarded by one of the following eight departments: Aeronautics and Astronautics, Chemical Engineering, Civil and Environmental Engineering, Earth Atmospheric and Planetary Sciences, Materials Science and Engineering, Mathematics, Mechanical Engineering, and Nuclear Science and Engineering; the specialization in computational science and engineering is highlighted by specially crafted thesis ﬁelds.
Sub-communities within this community
The MIT CDO SM program was renamed the Computational Science & Engineering (CSE) SM program in Spring 2020.
Collections in this community
Leveraging the Linear Response Theory in Sensitivity Analysis of Chaotic Dynamical Systems and Turbulent Flows (Massachusetts Institute of Technology, 2023-06)The linear response theory (LRT) provides a set of powerful mathematical tools for the analysis of system’s reactions to controllable perturbation. In applied sciences, LRT is particularly useful in approximating parametric ...
(Massachusetts Institute of Technology, 2023-06)Monte Carlo neutron transport is the gold standard for accurate neutronics simulation of nuclear reactors in steady-state because each term of the neutron transport equation can be directly tallied using continuous-energy ...
(Massachusetts Institute of Technology, 2022-09)Multinomial logit (MNL) model is widely used to predict the probabilities of different outcomes. However, standard MNL model suffers from several issues, including but not limited to heterogeneous population, the restricted ...