Academic Programs at New Mexico Tech

• Mathematics PhD Graduate Program
  • Founded over 10 years ago
• Biomedical Sciences Undergraduate Program
  • Founded Fall 2015

Graduate Courses at New Mexico Tech

• MATH 532, 532D, Perturbation Methods, 3 cr, 3 cl hrs
  • Prerequisite: MATH 437 or equivalent
  • A survey of expansion techniques. Regular and singular perturbations. Poincaré‐Linstedt method. Matched asymptotic expansions. Multiple scales.
• MATH 537, 537D, Bifurcation Theory, 3 cr, 3 cl hrs
  • Prerequisite: MATH 437 or equivalent
  • Discrete and continuous models. Nonlinear buckling, expansion of the bifurcated solution, stability analysis, Hopf bifurcation, degree theory, the Rabinowitz theorem, and other topics.
• MATH 539, 539D, Fluid Dynamics, 3 cr, 3 cl hrs
  • Prerequisite: MATH 438 or equivalent
  • The Navier‐Stokes equations, inviscid flow, irrotational fluids, viscosity, and turbulence. Other topics as time and interest permit.
• MATH 540, Calculus of Variations, 3 cr, 3 cl hrs
  • Prerequisite: MATH 437 or graduate standing
  • Development of the classical theorems of Calculus of Variations, application, some numerical approaches. Include Euler equations, broken extremals an the Weierstrass-Erdmann conditions, the second variation and Hamilton-Jacobi equation, the Weierstrass E-function, and the Ritz method.
• ST 557 Fractals and Chaos, 2 cr
  • Prerequisite: ST 550/550BD or departmental waiver
  • This course cover the development of the basic geometry of fractals, using both deterministic and random methods, the mathematical ideas behind chaos, the connections between the ideas of chaos and fractals, and applications.
• Mathematical Modeling - Math 530 (Modeling Case Studies), and ST 554 (Modeling for Teachers)
• Mathematical Biology Class - Math 531

Undergraduate Course

• MATH 430, Mathematical Modeling, 3 cr, 3 cl hrs
  • Prerequisites: MATH 335; one of MATH 254 or MATH 337; MATH 382, each passed with grade C‐ or better
  • Introduction to the process of developing, analyzing, and refining mathematical models. Deterministic and probabilistic models considered for both discrete and continuous problems. Applications to a variety of fields.