The Data-Enabled Computational Engineering and Science (DECES) program targets students with recently obtained Bachelor of Science (BS) degrees in Engineering, Applied Mathematics, Computer Science, Physical Sciences, and related disciplines, who are interested in pursuing careers that involve advanced modeling and simulation in engineering and physical sciences. This program will also be of interest to research staff as well as working professionals whose success on the job depends on their ability to perform high-fidelity engineering simulations with data assimilation. Data-Enabled Computational Engineering and Science is an inherently interdisciplinary field requiring in-depth knowledge of advanced mathematics, numerical methods and their computer implementation, engineering sciences, and methods in the emerging field of Data Science. Given the composition of Brown's School of Engineering and Applied Math faculty, we are uniquely positioned to offer such a program using a combined Engineering and Applied Math graduate curriculum.
Program Requirements:
| Two courses in Engineering, such as: | 2 | |
| Advanced Mechanics of Solids | ||
| Mathematical Methods in Engineering and Physics I | ||
| Mathematical Methods in Engineering and Physics II | ||
| Continuum Mechanics | ||
| Mechanics of Solids | ||
| Computational Methods in Structural Mechanics | ||
| Thermodynamics of Materials | ||
| Pattern Recognition and Machine Learning | ||
| Fluid Mechanics I | ||
| Fluid Mechanics II | ||
| Scientific Programming in C++ | ||
| Deep Learning for Scientists and Engineers | ||
| Image Understanding | ||
| Quantum Optics | ||
| Special Projects, Reading, Research and Design | ||
| Atomistic Modeling of Materials | ||
| Two courses in Applied Mathematics, such as: | 2 | |
| Computational Probability and Statistics | ||
| Deep Learning for Scientists & Engineers | ||
| Nonlinear Dynamical Systems I | ||
| Numerical Solution of Partial Differential Equations I | ||
| Numerical Solution of Partial Differential Equations II | ||
| Numerical Solution of Partial Differential Equations III | ||
| Computational Fluid Dynamics | ||
| Theory of Probability I | ||
| Introduction to Parallel Computing on Heterogeneous (CPU+GPU) Systems | ||
| Two courses in data science/high performance computing | 2 | |
| Two additional courses. To ensure depth these may be taken in Engineering, Applied Mathematics, Data Science, or other relevant disciplines. | 2 | |
| Total Credits | 8 | |
For more information on admission and program requirements for the Data-Enabled Computational Engineering and Science program, please visit https://computational.engineering.brown.edu/
Please view sample course plans based on Sc.M. Thesis or Non-Thesis and Curriculum options at https://computational.engineering.brown.edu/program-and-schedule