Edited by Dr.Douglas Allaire, 2016, 2017, 2022
Edited by Kaiyu Li, 2016, 2017
Edited by Anyone afterwards, write your name here.

Teaching

MEEN 357 – Engineering Analysis for Mechanical Engineers
Practical foundation for the use of numerical methods to solve engineering problems; error estimation, Taylor series, numerical solution of linear and non-linear algebraic and differential equations; introduction to engineering optimization.

MEEN 401 – Introduction to Mechanical Engineering Design
The design innovation process; need definition, functional analysis, performance requirements and evaluation criteria, conceptual design evaluation, down-selected to an embodiment; introduction to systems and concurrent engineering; parametric and risk analysis, failure mode analysis, material selection, and manufacturability; cost and life cycle issues, project management.

MEEN 423 – Machine Learning for Mechanical Engineers
Machine learning techniques with applications to the analysis and design of mechanical, fluid, thermal, material and multidisciplinary systems; linear and kernel support vector machines; neural networks; Bayesian techniques; decision trees and random forests; dimension reduction and model selection; data management and learner validation strategies; tools and application studies.

MEEN 602 – Modeling and Analysis of Mechanical Systems
State spaces and vector algebra with applications to static, dynamic and controls systems, state evolution, trajectories, ordinary differential equations; global and local balance laws and vector calculus to describe flowing/deforming systems; steady state and transient PDEs, statics and vibrations of strings and membranes, and the heat equation; numerical methods.

MEEN 683 – Multidisciplinary System Analysis and Design Optimization
Overview of principles, methods and tools in multidisciplinary system analysis and design optimization; engineering systems modeling for analysis, design and optimization; design variable selection, objective functions and constraints; subsystem identification and interface design; gradient-based and heuristic search methods; multi-objective optimization and Pareto optimality.
Referred from Texas A&M University Mechanical Engineering Department Undergraduate Course Description and Graduate Course Description.