INTRODUCTION TO SCIENTIFIC COMPUTING FOR ENGINEERING PHYSICS

COMPSCI/MATH 715

Introduction to spectral methods (Fourier, Chebyshev, Fast Fourier Transform), finite element methods (Galerkin methods, energy estimates and error analysis), and mesh-free methods (Monte-Carlo, smoothed-particle hydrodynamics) for solving partial differential equations. Applications from science and engineering. Applications from science and engineering. Students are strongly encouraged to have programming skills (e.g.COMP SCI 200), undergraduate numerical analysis (e.g.MATH/​COMP SCI  514orCOMP SCI 412), analysis (MATH 322and521) and linear algebra (e.g.MATH 341or equiv.)

EP 602

Subject matter, credits and prerequisites vary.

EP 271

Solution of engineering problems using commercially-available software tools (spreadsheets, symbolic manipulators, and equation solvers). The emphasis will be on nuclear engineering problems, including radioactive decay, nuclear cross sections, scattering, and criticality.

EMA/EP 471

Use of computational tools for the solution of problems encountered in engineering physics applications. Topics covered include orbital mechanics, structural vibrations, beam and plate deformations, heat transfer, neutron diffusion, and criticality. Emphasis will be on modeling, choice of appropriate algorithms, and model validation.

ME 737

Key computational topics for engineering applications will be discussed, encompassing both established classical numerical methods and the emerging field of machine learning. Knowledge of calculus [such asMATH 221], linear algebra and differential equations [such asMATH 320], probability [such asMATH 331] and programming in Python or MATLAB [such asCOMP SCI 220] is required.