Prof. Dan Ernst

Department of Computer Science

CS 170:Computing for the Sciences

and Mathematics

Welcome to CS 170 !

Instructor Dan Ernst

Office: P 139 ernstdj@uwec.edu

Course Web Site: http://www.cs.uwec.edu/~ernstdj/courses/cs170/ https://uwec.courses.wisconsin.edu

D2L - for grades

Introductions

My background PhD Michigan, 2005,High-performance computer

architecture

Mentoring undergraduates Teach computer architecture and systems software Multiple collaborative research projects w/science and math Director, UWEC Center for Computational Science *

Outside Workshops for the Supercomputing Education Program and

National Computational Science Institute

Your Turn

What is your name?

Where are you from?

What year you are in your studies?

What is your favorite use for computers?

Course Website

http://www.cs.uwec.edu/~ernstdj/courses/cs170/

The web site is an integral part of the course

Website will provide Lab/assignment information and due dates Exam information, dates & times Lecture slides

be sure to take your own notes! Samples from class

Getting Information

Read your e-mail! Class e-mail group will be used by the instructor Membership mandatory

I assume you read everything sent to this list!

Getting Help:When you have questions

Regarding HELP with course materials and assignments Come to office hours – Phillips 139

Wednesday 1 – 3 pm Thursday 9:30 – 11:30 am OR by appointment (just e-mail or call my office)

Send me an e-mail: ernstdj@uwec.edu

What is CS 170?

An introduction to the application of computational resources to solving problems in the sciences and mathematics

Goals: Knowledge:

Understand the modeling process Work with some standard modeling techniques

Skills: Gain experience in working with computational tools

(Vensim, Maple, etc.)

What This Course is About

This course is NOT about: Programming general applications (take CS 163/145) Making web pages – (take CS 318)

Ingredients of designing and writing software Computer programs A programming language (C++) Elementary programming concepts

Universal constructs Useful techniques

Producing (software) solutions to problems How to think about problems How to articulate solution in a precise way How to understand what a program is doing

Textbooks

Required: Shiflet and Shiflet

Introduction to Computational Science: Modeling and Simulation for the Sciences.

Overall Course Grading

Final Grade

: Exams (3):  40%

Weekly Assignments(10-12): 40%

Larger Projects(2): 20%

Assignment Deadlines

Assignments On-Time: Always due by the beginning of class on the

due date specified No assignment will be accepted after the deadline,

unless given prior approval Exceptions granted only under the most extraordinary

circumstances Advice: PLAN for things to go wrong!

CS 170 Academic Honesty

All work is to be done individually unless otherwise instructed We will do significant group work in this course as well

Discussing concepts is generally OK, exchanging or copying files or written work is NEVER OK

course materials provided by the instructor (textbook, handouts, project write-ups, course web pages, etc.) may be used freely in your work downloading code you found with google is NOT ok

Come see me for help if you have any uncertainty

Computer Accounts & Software

Your UWEC account is all that is needed

Major Software Vensim PLE

Available to you (free) at all Windows lab computers on campus Free download - http://www.vensim.com/venple.html

Maple Available to you (free) at all Windows lab computers on campus

Supported Platforms: Windows your programs must run with the campus installation to be

graded

Prof. Dan ErnstDepartment of Computer Science

CS 170:Computing for the Sciences

and Mathematics

Science

What is Science?

(A) A set of facts (B) Something that professional scientists do (C) The underlying Truth about the Universe (D) None of the above

(A) A set of facts … and (C)

We are constantly making new discoveries and collecting new data

Technology and experiments are changing

Old Theories are replaced by new Theories

Are any Theories not science? Even if they’re shown to be wrong later?

(B) Something Professional Scientists Do…

What is a scientist?

Do you need a PhD?

Amateur Scientists play an important role in discovery

Being scientific DOES NOT require a Union Card

Hanny’s Voorwerp

"Teacher finds new cosmic object", BBC, 5 August 2008

What is the Scientific Method?

What are the steps in the scientic method? Take two minutes to write down the steps we use in science.

Steps in Scientific Method

Characterization of existing data Current Theories

Formulation of a hypothesisDeduction - formulation of a predictive testExperimental testingError elimination and characterizationValidate or revise hypothesis

New Theories!

Science – Old School

Science – Newer Picture

Why Simulate?

Experimental Investigation

Record observations and make predictions

Example: Tycho Brahe (1546-1601) Observed and recorded the motions and

positions of the sun, moon, and Mars over a period of 20 years

Invented the sextant, mural quadrant, and portable ring armillary

Based on his records, he could predict future planetary positions

Theoretical Investigation

Theory: logical, self-consistent model for explaining a natural or social behavior Attempts to explain why things happen

as they do using the language of mathematics

Examples: Isaac Newton's (1643 – 1727) law of

gravitational attraction: "The gravitation attraction force between two point masses is directly proportional to the product of their masses and inversely proportional to the square of their separation distance."

Einstein's theory of relativity: energy and mass are equivalent and transmutable

e = mc2

Computational Investigation

Uses experimental and theoretical knowledge to create computational models of aspects of nature, then uses these models to simulate natural behavior and make predictions

Example: Johannes Kepler (1571 – 1630) Worked with Tycho Brahe's data Attempted to find patterns behind the data of planetary

motion that could be expressed mathematically

Modeling

“Application of methods to analyze complex, real-world problems in order to predict what might happen with some course of action”

Notes:No mention of “Computers” or “Computation”No mention of “Science”!

Modeling is about mathematics

Steps of Modeling Process

Analyze problemFormulate a modelSolve modelVerify and interpret model's solutionReport resultsMaintain/revise model

Computational Investigation Approach:

1. Identify the problem Copernicus's theory of circular planetary orbits didn't fit the data

2. Pose the problem in terms of a mathematical model Kepler started by assuming circular orbits, but that the sun was NOT

at the center He then guessed at different equations using trial and error

3. Test the model using a computational method He did the calculations by hand and it took him 5 years…

4. Assess the results Does the solution make theoretical sense? Does the solution fit the data within a tolerable degree of error?

Kepler's Findings:

1. Planetary motion is an ellipse with one focus at the sun

Kepler's Findings:

2. The line between a planet and the sun sweeps out equal areas in equal times

Kepler's Findings:

3. The square of a planet's orbital period is proportional to the cube of its average distance from the sun:

T2 = cr3

Computational Investigation Approaches

Do the calculations by hand Time-consuming, error-prone

Use a digital computer Fast But only as good as the underlying model,

method, and implementation!

"If you put tomfoolery into a computer, nothing comes out but tomfoolery. But this tomfoolery, having passed through a very expensive machine, is somehow ennobled and no one dares criticize it." - Pierre Gallois

Computational Science

Classes of Models

Static Model Does not take time into account

Dynamic Model Models changes over time

Classes of Models

Stochastic Model Some element of probability/chance involvedhttp://www.sfgate.com/cgi-bin/blogs/ybenjamin/detail?entry_id=71442 Need to run model many times with different random

values

Deterministic Model No probability/chance involved Run once

Steps of Modeling Process

Analyze problem Be precise!

Formulate a model Gather data Make simplifying assumptions and document them Determine variables and units Establish relationships among variables and

submodels Determine equations and functions

Steps of Modeling Process

Solve model

Verify and interpret model's solution verification determines if solution works correctly validation establishes if system satisfies problem's

requirements

Steps of Modeling Process

Report on model Analysis of problem Model design Model solution Results and conclusions

Maintain model

HOMEWORK!

READ pages 3-10 in the textbookPerform the modeling process described there

With a partner

Problem: For a group of n people, how many pizzas do I need?

Factors to consider… Size of pizzas (radius), # of slices, shape of slices, crust style… Toppings, appetite, size of participants…

Steps you can mostly skip: 4 (mostly), 6Report on each step!

graph pizza vs. n for various parameters in step 3, etc.