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Master’s in Data-Enabled Computational Engineering and Science

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Master’s in Data-Enabled Computational Engineering and Science

Program and Schedule

A modern computational engineering scientist must be educated in applied mathematics, understand the foundations of data science, be comfortable with programming and carrying out parallel computations, and have deep expertise in one or more engineering areas.

Program and Schedule

A modern computational engineering scientist must be educated in applied mathematics, understand the foundations of data science, be comfortable with programming and carrying out parallel computations, and have deep expertise in one or more engineering areas.

Program of study

Students will take a total of eight courses to satisfy the degree requirements. To ensure breadth, students are expected to take at least two courses in engineering, at least two courses in applied mathematics, and at least two courses in data science/high performance computing. This leaves two more courses to be taken to satisfy the program requirements, and, to ensure depth, these may be taken in engineering, applied mathematics, data science, or other relevant disciplines.

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Master of Science – Thesis Option

Candidates must complete a coherent plan of study based in data-enabled computational engineering and science consisting of eight graduate or advanced level courses and an acceptable thesis, which is normally sponsored by a member of the Engineering or APMA faculty.

  • Students are expected to complete the Master of Science – Thesis program option in three or four semesters. 
  • In the three-semester format, the students are expected to take three courses in the first semester, three in the second semester, and two in the third semester. 
  • However, in the second and third semesters, the students may sign up for a “Special Topics: Reading Research and Design” type class (i.e., ENGN 2980 or equivalent in APMA) to satisfy the eight-course requirement. 
  • In the four-semester model, students are expected to take three courses in the first semester, two in the second semester, two in the third semester, and one in the fourth semester. 
  • In both cases, the “Special Topics: Reading Research and Design” course may be counted up to two times toward the degree.

Students should choose courses in consultation with the student's advisor to develop a coherent program. The proposed program of study must be approved by the Academic Director of Data-Enabled Computational Engineering and Science Master’s Program in the School of Engineering.

For students in a Data-Enabled Computational Engineering and Science program (Thesis Option), the approved course sequence is 2-2-2-2, where the student takes two courses in each semester. However, the program strongly recommends a sequence of 3-2-2-1 where the student takes 3 courses the first semester, 2 the second, 2 the third, and 1 the fourth. Any deviation from this schedule without consultation with the program advisor can result in additional tuition and/or penalties.

Master of Science – Non-Thesis Option

Candidates must complete a coherent plan of study based in data-enabled computational engineering and science consisting of eight graduate or advanced level courses.

  • Students are expected to complete the Master of Science – Non-Thesis program option in three semesters, taking three courses in the first and second semesters, and two courses in the third semester (i.e., the 3-3-2 model). 
  • One-year completion is also possible with students taking four courses in the first semester and four courses in the second (i.e., the 4-4 model). The maximum duration students may take to complete will be four semesters.

Students should choose courses in consultation with the student's advisor to develop a coherent program. The proposed program of study must be approved by the Academic Director of Data-Enabled Computational Engineering and Science Master’s Program in the School of Engineering.

For students in the Data-Enabled Computational Engineering and Science program (Non-Thesis Option), the approved course sequence is 3-3-2, meaning the student takes 3 courses the first semester, 3 the second, and 2 the third. Any deviation from this schedule without consultation with the program advisor can result in additional tuition and/or penalties.

Master of Science (Non-Thesis Professional Option)

Candidates must complete a coherent plan of study based in data-enabled computational engineering and science consisting of eight graduate or advanced level courses. In addition to the course requirements, a paid or unpaid internship is a required component of the professional-track program. Assistance in obtaining internships will be provided by the School and the Brown Center for Career Exploration.

  • Students are expected to complete the Master of Science – Non-Thesis program option in three semesters, taking three courses in the first and second semesters, and two courses in the third semester (i.e., the 3-3-2 model). 
  • The maximum duration students may take to complete will be four semesters (i.e., the 2-2-2-2 model).
  • A paid or unpaid experiential learning experience of 3-6 months is a required component of the professional track program. Experiential learning can include one of the following (but not both):
    • A summer internship directly related to the program of study
    • Completion of  ENGN 2960 (Experiential Learning in Industry (ELI)) as an elective course that counts towards the eight-course requirement. Note: students enrolled in ENGN 2960 are considered full-time students and may be counted only one time toward the degree.

Students should choose courses in consultation with the student's advisor to develop a coherent program. The proposed program of study must be approved by the Academic Director of Data-Enabled Computational Engineering and Science Master’s Program in the School of Engineering.

For students in the Data-Enabled Computational Engineering and Science program (Professional Option),  the approved course sequence is 2-2-2-2, meaning the student takes 2 courses the first semester, 2 the second, 2 the third, and 2 the fourth. Any deviation from this schedule without consultation with the program advisor can result in additional tuition and/or penalties.

Sample Course Plans

Courses could be scheduled according to the following sample plans.

Outcome is acceptance into PhD program

Thesis Option

Three Semesters (2+3+3)

Courses

Fall Semester

Spring Semester

Year 1

ENGN 2912Q Chemically Reactive Flow

ENGN 2911R Analytical Modeling for Biomechanical and Biomedical Systems

CSCI 2952C Learning with Limited Label Data

APMA 2550 Numerical Solution PDE I

ENGN 2980 Special Projects: RRD

Year 2

APMA 1860 Graphs and Networks 

APMA 2070 Deep Learning

ENGN 2980 Special Projects: RRD

 

 

Outcome is acceptance into PhD program

Thesis Option

Three Semesters (3+2+3)

Courses

Fall Semester

Spring Semester

Year 1

CSCI 2470 Deep Learn

APMA 2550 Numerical Solution PDE I

ENGN 2210 Continuum Mechanics

APMA 2070 Deep Learning 

APMA 2560 Numerical Solutions PDE II

Year 2

ENGN 2340 Comp Methods Structural Mechs

ENGN 2980 Special Projects: RRD ENGN 2980 Special Projects: RRD

 

 

Outcome is acceptance into National Lab

Non-Thesis Option

Four Semesters (2+2+2+2)

Courses

Fall Semester

Spring Semester

Year 1

ENGN 2920M Materials of interface for energy storage devices

PHYS 2020 Mathematical Methods for engineers and physicists

APMA 2070 Deep Learning for scientists

ENGN 2930 Atomistic Modeling of Materials

Year 2

APMA 2812F Advanced Topics in Stochastic Process

PHYS 2470 Advanced Statistical Methods

PHYS 2600 Computational Physics

APMA 2120 Hilbert Space and Applications

 

Outcome is acceptance into Industry Consulting

Thesis Option

Three Semesters (2+3+3)

Courses

Fall Semester

Spring Semester

Year 1

ENGN 1300 Structural Analysis

ENGN 1740 Computer Aided Visualization

ENGN 2340 Comp Methods Structural Mechs

APMA 2550 Numerical Solution PDE I

ENGN 2912B Scientific Programming in C++

Year 2

ENGN 2520 Pattern Recognition and Machine Learning

CSCI 2470 Deep Learning

ENGN 2020 Mathematical Methods in Engineering and Physics II

 

 

The following example schedule, which will have the student graduate in three semesters, applies to incoming students with sufficient preparation in applied mathematics:

 FallSpring
Year 1ENGN2210 - Continuum Mechanics
ENGN2340 - Computational Methods in Structural Mechanics
APMA2550 - Numerical Solution of PDEs I
ENGN2220 - Mechanics of Solids 
APMA2560 - Numerical Solution of PDEs II
APMA2822B - Introduction to Parallel Computing on Heterogeneous (CPU+GPU) Systems
Year 2ENGN2520 - Pattern Recognition and Machine Learning
APMA2630 - Theory of Probability I
 

The following example schedule, which will have the student graduate in three semesters, applies to incoming students with sufficient preparation in applied mathematics:

 Fall      Spring      
Year 1      ENGN2210 - Continuum Mechanics,         
ENGN2810 - Fluid Mechanics I          
APMA2550 - Numerical Solution of PDEs I      
ENGN2020 - Math Methods in Engineering and Physics II          
ENGN2820 - Fluid Mechanics II          
APMA2560- Numerical Solution of PDEs II      
Year 2      ENGN2520 - Pattern Recognition and Machine Learning         
APMA2580A/C - Computational Fluid Dynamics/Optimization      
 

The following example schedule, which will have the student graduate in three semesters, applies to incoming students with sufficient preparation in applied mathematics:

 Fall      Spring      
Year 1      ENGN2210 - Continuum Mechanics         
ENGN2410 - Thermodynamics of Materials         
APMA2550 - Numerical Solution of PDEs I      
ENGN2020 - Math Methods in Engineering and Physics II          
ENGN2930 - Atomistic Modeling of Materials         
APMA2560 - Numerical Solution of PDEs II      
Year 2      ENGN2520 - Pattern Recognition and Machine Learning         
APMA2630 - Theory of Probability I      
 

If a student feels more preparation might be needed before moving into the advanced courses, especially in the areas of Applied and Computational Mathematics, a recommended schedule of courses to complete the DECES degree in three semesters may look like:

 Fall      Spring      
Year 1      ENGN1750 - Advanced Mechanics of Solids         
APMA1690 - Computational Probability and Statistics         
ENGN2210 - Continuum Mechanics      
ENGN2020 - Math Methods in Engineering and Physics II          
ENGN2220 - Mechanics of Solids         
APMA2822B - Introduction to Parallel Computing on Heterogeneous (CPU+GPU) Systems      
Year 2      ENGN2520 - Pattern Recognition and Machine Learning         
APMA2550 - Numerical Solution of PDEs I      
 
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Providence RI 02912 401-863-1000

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