Prepare for a career developing model-based simulations and designs
Duke’s Master of Engineering in Computational Mechanics and Scientific Computing is one of the most comprehensive in the world—and features a top-notch faculty.
We offer significant support for competitive applicants—typical scholarships range from $20,000-$30,000.
"The problem-solving skills I learned were the most valuable part of my Duke experience."
Huidi Ji |Senior Engineer, SIMULIA Research and Development Huidi's Story »
Increasingly, engineering systems are being designed and tested virtually. The successful use of model-based simulation in modern applications requires a solid background in engineering physics, computer science, probability, data sciences, and applied mathematics. This Master of Engineering program provides a strong foundation in all of these areas.
The program emphasizes the use and development of modern numerical tools for model-based simulations such as finite element methods, uncertainty quantification procedures, and data analysis techniques, among others.
We offer a large number of core and elective courses in finite element methods for applications in solid mechanics, fluid mechanics, and coupled field problems.
In the Duke Master of Engineering program, you take specialized technical classes and a core of business leadership and management courses, with a required internship or a project completing the degree.
We offer significant support for competitive applicants—typical scholarships range from
Degree requirements, detailed below, include:
- 30 course credits
- 1 Seminar
- Required Internship
- For fall entry, visit the Duke Engineering MEng website
Who Should Consider Applying?
The MEng in Computational Mechanics and Scientific Computing is ideal for students who want to pursue careers in:
- Biomedical device design
- Automotive simulations/crashworthiness
- Noise reduction in vehicles; mobile device design and vibration analysis
- Infrastructure design and analysis
- Additive manufacturing
- Industrial product design
You should consider this degree if you have an engineering background and are interested in careers in computational modeling in engineering.
If you have a background in physics, computer science, or mathematics and are interested in enhancing your knowledge and understanding applications in engineering and practical modeling techniques, this degree may be for you.
How This Program Will Prepare You for a Career
- Engineering positions in model-based simulation and design increasingly require master's-level training.
- Students who complete this program will be well-prepared to use finite element methods and other modern numerical tools to model problems in additive manufacturing, engineering mechanics, and engineering consulting.
- Students completing the program will be well-prepared to continue on in PhD programs in computational science and engineering.
- Students receive interdisciplinary training in modern computational methods, engineering, computer science, and applied math.
Our Graduates Are Working At:
- Major corporations, such as 3M
- Hedge funds
- Engineering consulting firms, such as Boston Consulting Group
- Public-sector research positions, such as Sandia National Laboratory
Our interdisciplinary faculty bring experience and expertise from mechanical engineering, civil engineering and computer science, including:
Aquino has broad interests in computational mechanics, including finite element methods, computational inverse problems, uncertainty quantification, coupled chemo-mechanics, and computational acoustics, among others.
Blum's research focuses on "first-principles" computational materials science: using the fundamental laws of quantum mechanics to predict the properties of real materials from the atomic scale on upwards.
Dolbow's research concerns the development of computational methods for nonlinear problems in solid mechanics.
Guilleminot’s research focuses on uncertainty quantification, computational mechanics and materials science, as well as on topics at the interface between these fields.
Scovazzi’s research interests include finite element and advanced numerical methods for computational fluid and solid mechanics.
Veveakis’s research interests include geomechanics, theoretical and applied mechanics, and thermodynamics, with an emphasis in multiphysical modeling of plasticity of solids, solid-fluid interactions, friction laws and rheology of geomaterials.
The Master of Engineering in Computational Mechanics and Scientific Computing is a 30-credit degree distributed as follows:
- Core Industry Preparation Courses (6 credits)
- Departmental/Disciplinary or Cross Disciplinary Requirements (12 credits)
- Technical Electives in a Concentration Area (12 credits)
- Internship, Project or Equivalent (0 credits)
- Curriculum Overview
I. Core Requirements (6 credits / 2 courses)
- MENG 540: Management of High Tech Industries (3 credits)
- MENG 570: Business Fundamentals for Engineers (3 credits)
II. Proseminar (0.0 credits / 1 course)
- CE 703 /ME 703: Industrial Colloquia in ComputationalMechanics and Scientific Computing
III. Finite Element Methods (6 credits / 2 courses)
- CE 530 / ME 524: Introduction to the Finite Element Method
- CE 630 / ME 525: Nonlinear Finite Element Method
IV. Applied Math/Statistics ( 3 credits / 1 course from list)
- Math 561: Numerical Linear Algebra
- Math 541: Applied Stochastic Processes
- Math 551 Applied Differenential Equations and Complex Variables
V. Computer Science ( 3 credits / 1 course from list)
- CS 590: Parallel Computing
- ECE 551D: Programming, Data Structures, and Algorithms in C++
VI. Application Areas ( 4 of any courses from three areas of concentration, 12 credits)
Mechanics of Materials
- CEE 520: Continuum Mechanics
- CEE 541: Structural Dynamics
- ME 555: Computational Materials Science
- CEE 622: Fracture Mechanics
- CEE 531: Finite Element Methods for Problems in Fluid Mechanics
- ME 572: Engineering Acoustics
- ME 639: Computational Fluid Mechanics and Heat Transfer
- CEE 690: Turbulence
Optimization / Data Analytics
- CS-445/MATH-465: Intro to High Dimensional Data Analysis
- CE 522 / ME 526: Numerical Optimization
- CS-571D: Machine Learning
- STA 502: Bayesian Inference and Decision
Other course offerings may be substituted with consent of the Director of Masters Studies.
VII. Internship, Project or Equivalent Requirements (0 credits)
- MENG 550: Internship or Applied Research Project
- MENG 551: Internship/Project Assessment
Sample Computational Mechanics and Scientific Computing Curriculum (No Specialization)
Fall Year 1
Spring Year 1
Summer Year 1
Fall Year 2
Core Industry Preparation Courses
MENG 570:Business Fundamentals for Engineers
MENG 540:Leadership & Management Principles for Technology-Based Organizations
MENG 550:Internship or Applied Research Project
MENG 551:Internship/Project Assessment
CEE 530 / ME 524: Introduction to the Finite Element Method
CEE 630 / ME 525:Nonlinear Finite Element Method
Applied Math / Statistics course
Computer Science Course