The Engineering Mechanics (EM) graduate program at The University of Texas at Austin prepares Master of Science and Doctor of Philosophy students for continued work in academia and industry. Graduates are equipped to solve technical problems in a wide range of fields including aerospace, automotive, petroleum, manufacturing, and computer engineering to name a few. Our faculty possess a broad range of expertise in experimental, theoretical, and computational mechanics. We offer advanced study and research leading to the Master of Science in Engineering degree and the Doctor of Philosophy degree in engineering mechanics. The normal prerequisite for graduate study is a Bachelor of Science degree in engineering mechanics or in a related field of engineering. Graduate study is possible for those with degrees in science or mathematics, but some undergraduate coursework will be needed to make up any deficiencies.
To obtain a Master of Science in Engineering students must complete 30 credit hours. Students enrolled in the Thesis/Report option will complete either 24 hours of coursework plus 6 hours of supervised research thesis (ASE or EM 698A and 698B) or 27 hours of coursework plus 3 hours of supervised research report (ASE or EM 398R). Up to 6 hours of upper-division undergraduate coursework may be included in the required coursework. A faculty advisor is chosen by the student with the agreement of the advisor. The student's advisor will approve the Program of Work (PoW) and supervise their research. In the case of the Master's thesis, the supervised research must be taken in two consecutive semesters. For both the Thesis and the Report, the final research course must be taken in the semester of graduation.
This area involves study and research on theoretical and implementational aspects of numerical simulations: applied mathematics (functional analysis, partial differential equations, dynamical systems), numerical analysis (a- priori and a-posteriori error estimation, adaptive algorithms, stochasticity), computer science (high performance linear algebra, parallel computing), software engineering (programming in Fortran 90, data structures), data science, artificial intelligence and machine learning, visualization and geometry modeling, and mathematical modeling of multiscale, multiphysics problems.
