PHYS 3101
Spring semester 2019
  • Syllabus
  • Calendar
    • HW01
    • HW02
    • HW03
    • HW04
    • HW05
    • HW06
    • HW07
    • HW08
    • HW09
    • Homework guidelines
    • Homework grades
  • Downloads
    • Midterm 1
    • Midterm 2
    • Final exam
    • Exam grades

Numerical computing

  • Cleve Moler (MathWorks), Numerical Computing with MATLAB

    Numerical Computing with MATLAB is a textbook for an introductory course in numerical methods, ..., and technical computing. The emphasis is on informed use of mathematical software. We want you learn enough about the [available software] that you will be able to use [it] correctly, appreciate [its] limitations, and modify [it] when necessary to suit your own needs.

  • L. N. Trefethen (University of Oxford), Scientific Computing for DPhil (PhD) Students , 2016

    Videos of twenty four 50-minute lectures for DPhil (PhD) students across all the science departments at the University of Oxford.

  • Peter J. Olver (University of Minnesota), Lecture Notes on Numerical Analysis , 2008

    "The goal of this course of lectures is to introduce some of the most important and basic numerical algorithms that are used in practical computations ... [with] a thorough understanding of how the algorithms are constructed, why they work, and what their limitations are."

  • Victor Eijkhout (University of Texas at Austin), Introduction to High-Performance Scientific Computing , 2017

    "While good texts exist on numerical modeling, numerical linear algebra, computer architecture, parallel computing, performance optimization, no book brings together these strands in a unified manner. The need for a book such as the present became apparent to the author working at a computing center: users are domain experts who not necessarily have mastery of all the background that would make them efficient computational scientists. This book, then, teaches those topics that seem indispensible for scientists engaging in large-scale computations"

  • Masayuki Yano (University of Toronto), Scientific Computing , 2017

    "Third-year undergraduate course at the University of Toronto introduces numerical methods for scientific computation which are relevant to the solution of a wide range of engineering problems. Topics addressed include interpolation, integration, linear systems, least-squares fitting, nonlinear equations and optimization, initial value problems, and partial differential equations."

  • Greg Fasshauer (Illinois Institute of Technology), Computational Mathematics I: Numerical Linear Algebra , 2006; Computational Mathematics II: Numerical Methods for Differential Equations , 2007

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\[ \begin{align} \frac{d}{d t} \frac{\partial {\mathcal L}}{\partial \dot{q}} & = \frac{\partial {\mathcal L}}{\partial q} \\ H & = \frac{\partial {\mathcal L}}{\partial \dot{q}}\dot{q}-{\mathcal L}\\ -\frac{\partial S}{\partial t} & = H\left(q, \frac{\partial S}{\partial q}, t\right) \end{align} \]

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Resources

  1. Classical mechanics
  2. Numerical computing
  3. Dimensional analysis
  4. Mathematical methods
  5. Latex
  6. Julia

Course Archives

  1. Mechanics I, Fall 2010
  2. Mechanics II, Spring 2014
  3. Math Methods, Spring 2017

Links

  1. UConn AnyWare
  2. UConn GitLab
  3. UConn VPN
  4. UConn large file sharing
  5. UConn software
  6. Babbidge Library (free) laptop rentals

General

  1. Academic Calendar, Spring 2019
  2. UConn Physics Department
  3. Dean of students
  4. 2019 Calendar of Religious Holidays
  5. Educational Rights and Privacy
  6. Office of the Provost's policies links

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