Specializations and Course Listings

Below you will find a list of Mechanical Engineering graduate specializations and a course listing for each area. In each specialization, students develop skills in the following: 

  • Design and Manufacturing - computer-aided design, optimal design, design with reliability, additive manufacturing, micro- and nano-fabrication

  • Dynamics and Mechatronics – electromechanical system dynamics, microstructural vibrations, rigid-body dynamics, MEMS/NEMS, novel acoustic measurement techniques, mechatronics, robotics, microscale vibrations and acoustics, nonlinear dynamics, sensors and actuators, control systems
  • Materials - materials characterization, microstructure/property relationships, thin films, computational materials, interfacial phenomena, functional materials, materials processing
  • Solid Mechanics and Mechanical Design- mechanics of materials and structures, computational mechanics, biomechanics, waves and vibration, computer-aided design, design optimization, design with reliability, design for additive manufacturing
  • Energy and Transport Phenomena - heat and mass transfer in biological/environmental/industrial applications, microfluidics/nanofluidics, complex fluids, mechanobiology, interfacial phenomena/wetting, additive manufacturing, energy generation, energy storage, energy efficient space heating and cooling, smart electronics and data center cooling, small-scale power harvesting

 Design and Manufacturing courses include:

ME 531 Applied Machine Learning for ME

This course covers machine learning fundamentals, some popular and advanced machine learning models. Major topics include supervised learning (logistics regression, support vector machine, artificial neural networks, Gaussian process), unsupervised learning (clustering, dimensionality reduction), convolutional neural networks, generative adversarial networks, physics-constrained/informed neural networks, and optimization algorithms (stochastic gradient descent, Bayesian optimization). This course also covers the applications of machine learning models in mechanical engineering. Students should be familiar with Python basic commands and programming. Prerequisites: ME 303 or equivalent.  Offered in the fall semester. 3 credits

ME 573X Micro/Nanomaterials Processing

This course will explore how micro and nano-scale materials and devices are produced. Covered topics include the 1) fundamentals of micro and nano-materials processing in material science and transport phenomena, 2) micro and nano-fabrication processes for Micro-Electro-Mechanical Systems (MEMS) and Nano-Electro-Mechanical Systems (NEMS), 3) existing and emerging manufacturing processes for industrial scale production of micro- and nano-scale materials, and 4) Metrology and characterization tools for conducting research in micro and nano-materials processing. This course is cross-listed as a graduate-level course. Completion of additional assignments is required for graduate credits. Prerequisites: ME 302 or approval of instructor. Term varies. 3 credits

Faculty associated with this: Chiarot, Ke, Liu, Murray, Park, Razavi, Schiffres, Zhang, Zhao

Dynamics and Mechatronics courses include:

ME 531 Applied Machine Learning for ME

This course covers machine learning fundamentals, some popular and advanced machine learning models. Major topics include supervised learning (logistics regression, support vector machine, artificial neural networks, Gaussian process), unsupervised learning (clustering, dimensionality reduction), convolutional neural networks, generative adversarial networks, physics-constrained/informed neural networks, and optimization algorithms (stochastic gradient descent, Bayesian optimization). This course also covers the applications of machine learning models in mechanical engineering. Students should be familiar with Python basic commands and programming. Prerequisites: ME 303 or equivalent.  Offered in the fall semester. 3 credits

Faculty associated with this: Gu, Homentcovschi, Miles, Pitarresi, Selleck, Towfighian, Younis, Yu, Zaychik, J. Zhou

Materials courses include:

ME 531 Applied Machine Learning for ME

This course covers machine learning fundamentals, some popular and advanced machine learning models. Major topics include supervised learning (logistics regression, support vector machine, artificial neural networks, Gaussian process), unsupervised learning (clustering, dimensionality reduction), convolutional neural networks, generative adversarial networks, physics-constrained/informed neural networks, and optimization algorithms (stochastic gradient descent, Bayesian optimization). This course also covers the applications of machine learning models in mechanical engineering. Students should be familiar with Python basic commands and programming. Prerequisites: ME 303 or equivalent.  Offered in the fall semester. 3 credits

ME 573X Micro/Nanomaterials Processing

This course will explore how micro and nano-scale materials and devices are produced. Covered topics include the 1) fundamentals of micro and nano-materials processing in material science and transport phenomena, 2) micro and nano-fabrication processes for Micro-Electro-Mechanical Systems (MEMS) and Nano-Electro-Mechanical Systems (NEMS), 3) existing and emerging manufacturing processes for industrial scale production of micro- and nano-scale materials, and 4) Metrology and characterization tools for conducting research in micro and nano-materials processing. This course is cross-listed as a graduate-level course. Completion of additional assignments is required for graduate credits. Prerequisites: ME 302 or approval of instructor. Term varies. 3 credits

Faculty associated with this: Cho, Liu, Murray, Singler, Zhao, G. Zhou

Solid Mechanics and Mechanical Design courses include:

ME 531 Applied Machine Learning for ME

This course covers machine learning fundamentals, some popular and advanced machine learning models. Major topics include supervised learning (logistics regression, support vector machine, artificial neural networks, Gaussian process), unsupervised learning (clustering, dimensionality reduction), convolutional neural networks, generative adversarial networks, physics-constrained/informed neural networks, and optimization algorithms (stochastic gradient descent, Bayesian optimization). This course also covers the applications of machine learning models in mechanical engineering. Students should be familiar with Python basic commands and programming. Prerequisites: ME 303 or equivalent.  Offered in the fall semester. 3 credits

Faculty associated with this: Ke, Park, Razavi, Schiffres, Wagner, Zhang

Energy and Transport Phenomena courses include:

ME 531 Applied Machine Learning for ME

This course covers machine learning fundamentals, some popular and advanced machine learning models. Major topics include supervised learning (logistics regression, support vector machine, artificial neural networks, Gaussian process), unsupervised learning (clustering, dimensionality reduction), convolutional neural networks, generative adversarial networks, physics-constrained/informed neural networks, and optimization algorithms (stochastic gradient descent, Bayesian optimization). This course also covers the applications of machine learning models in mechanical engineering. Students should be familiar with Python basic commands and programming. Prerequisites: ME 303 or equivalent.  Offered in the fall semester. 3 credits

Faculty associated with this: Chiarot, Daskiran, Gu, Huang, Liu, Murray, Sammakia, Singler, Schiffres, Tan