Concentration in Applied Mathematics & Scientific Computing
We offer a flexible and student-centered master's degree program in applied and computational mathematics, equipping our graduates with a strong foundation on which to build their careers (or pursue further graduate studies). Class sizes are small, and students receive individualized attention and mentoring. Both thesis and non-thesis degree options are available.
Current faculty specialties include: Applications of mathematics to the biological sciences (in particular to microbial communities and biofilms); mathematical approaches to composite materials and elasticity; mathematical and computational models for traffic flow and radiative transfer; fluid dynamics; scientific computing; matrix computations and numerical linear algebra; probability theory and applications; geometry and computation; partial differential equations; applications of partial differential equations to optics.
Convenient Scheduling. Students can choose either part-time or full-time study. Each semester's course offerings include late afternoon (4:00 pm or later) and early evening classes. Graduate courses in mathematics are primarily taught on Tue-Thur or Mon-Wed schedules. Classes are held on Temple's Main Campus.
For more information: Please contact us at [click-for-email].
Track: Applied Mathematics and Modeling
Philosophy and Goals
The track in Applied Mathematics and Modeling provides the student with a well-balanced training in applied mathematics, by combining mathematical analysis with mathematical modeling, key concepts in computing, and connections to other areas of science and engineering. At the end of their studies, students will be able to effectively work across disciplinary boundaries, transforming real-world problems into mathematics, and to apply tools of applied analysis and/or computing to solve those problems. The course MATH 8107 enables students to use those skills within projects posed by external partners.
Recommended Pathway of Courses
The following sequence of courses achieves the program goals. The first 4 courses listed below provide the basis for a degree via qualification exams (they are recommended to be taken in the first year). The alternative pathway via a MS thesis is supported via these courses as well.
MATH 5043. Numerical Analysis
MATH 5044. Numerical Methods for Ordinary Differential Equations
MATH 8041. Real Analysis I
MATH 8042. Real Analysis II
MATH 5057. Applied Differential Equations and Optimization
MATH 5058. Fundamentals of Mathematical Modeling
MATH 8141. Partial Differential Equations
MATH 8023. Numerical Methods for Partial Differential Equations
MATH 8107. Mathematical Modeling for Science, Engineering, and Industry
MATH 9210. Topics in Applied Mathematics (e.g., mathematical biology, multiscale modeling and methods, material science, or control theory); or one of the following courses:
MATH 8031. Probability Theory
MATH 8142. Intermediate Partial Differential Equations
MATH 9041. Functional Analysis
MATH 9042. Fourier Analysis and Distribution Theory
MATH 9043. Calculus of Variations
Track: Computational Mathematics
Philosophy and Goals
The track in Computational Mathematics provides the student with expertise in modern computational methodologies, combined with a rigorous training in mathematical analysis to study those methods. These core components are complemented by foundations in analysis and mathematical modeling to provide perspectives on how relevant mathematical problems arise in real-world applications. The course MATH 8107 enables students to use those skills within projects posed by external partners. The Center for Computational Mathematics and Modeling provides opportunities for interdisciplinary research with modern hardware like virtual reality. Research with partners in other areas of sciences is possible.
Recommended Pathway of Courses
The following sequence of courses achieves the program goals. The first 4 courses listed below provide the basis for a degree via qualification exams (they are recommended to be taken in the first year). The alternative pathway via a MS thesis is supported via these courses as well.
MATH 5043. Numerical Analysis
MATH 5044. Numerical Methods for Ordinary Differential Equations
MATH 8041. Real Analysis I
MATH 8042. Real Analysis II
MATH 8141. Partial Differential Equations
MATH 8023. Numerical Methods for Partial Differential Equations
MATH 8024. Numerical Methods for Nonlinear Partial Differential Equations
MATH 5057. Applied Differential Equations and Optimization
MATH 8013. Numerical Linear Algebra
MATH 8107. Mathematical Modeling for Science, Engineering, and Industry
One of the following courses:
MATH 9200. Topics in Numerical Analysis (e.g., numerical optimization, computational fluid dynamics, numerical methods for general flow problems, or advanced finite element methods)
MATH 5066. Mathematical Methods for High Performance Computing
An appropriate course in Computer Science, e.g., machine learning