tag:open.umn.edu,2005:/opentextbooks/subjects/appliedOpen Textbook Library - Applied Textbooks2023-09-28T22:14:51Zhttps://open.umn.edu/assets/common/favicon/favicon-1594c2156c95ca22b1a0d803d547e5892bb0e351f682be842d64927ecda092e7.icohttps://open.umn.edu/assets/library/otl_logo-f9161d5c999f5852b38260727d49b4e7d7142fc707ec9596a5256a778f957ffc.png14962023-09-28T22:32:41Z2023-10-09T14:28:37ZClassical Numerical Methods in Scientific Computing<img alt="Read more about Classical Numerical Methods in Scientific Computing" title="Classical Numerical Methods in Scientific Computing cover image" class="cover " width="300" height="350" data-controller="cover" data-placeholder="/assets/common/placeholder-0e0607cbc50663ddb9e8fd188058bcd2630c730ef6ee322801278607b7d5af8e.png" src="/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsiZGF0YSI6OTU4OCwicHVyIjoiYmxvYl9pZCJ9fQ==--735b24851aa129204bfdb36cb407784bc4c1ce3c/submission_67_84_coverImage_en_US_t.png" />Partial differential equations are paramount in mathematical modelling with applications in engineering and science. The book starts with a crash course on partial differential equations in order to familiarize the reader with fundamental properties such as existence, uniqueness and possibly existing maximum principles. The main topic of the book entails the description of classical numerical methods that are used to approximate the solution of partial differential equations. The focus is on discretization methods such as the finite difference, finite volume and finite element method. The manuscript also makes a short excursion to the solution of large sets of (non)linear algebraic equations that result after application of discretization method to partial differential equations. The book treats the construction of such discretization methods, as well as some error analysis, where it is noted that the error analysis for the finite element method is merely descriptive, rather than rigorous from a mathematical point of view. The last chapters focus on time integration issues for classical time-dependent partial differential equations. After reading the book, the reader should be able to derive finite element methods, to implement the methods and to judge whether the obtained approximations are consistent with the solution to the partial differential equations. The reader will also obtain these skills for the other classical discretization methods. Acquiring such fundamental knowledge will allow the reader to continue studying more advanced methods like meshfree methods, discontinuous Galerkin methods and spectral methods for the approximation of solutions to partial differential equations.14952023-09-28T22:10:35Z2024-01-22T15:05:54ZNumerical Methods in Scientific Computing<img alt="Read more about Numerical Methods in Scientific Computing" title="Numerical Methods in Scientific Computing cover image" class="cover " width="300" height="370" data-controller="cover" data-placeholder="/assets/common/placeholder-0e0607cbc50663ddb9e8fd188058bcd2630c730ef6ee322801278607b7d5af8e.png" src="/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsiZGF0YSI6OTU4NSwicHVyIjoiYmxvYl9pZCJ9fQ==--4252392e602cdff5e054f9e09f20c2fca0f75246/submission_70_87_coverImage_en_US_t.png" />This is a book about numerically solving partial differential equations occurring in technical and physical contexts and the authors have set themselves a more ambitious target than to just talk about the numerics. Their aim is to show the place of numerical solutions in the general modeling process and this must inevitably lead to considerations about modeling itself. Partial differential equations usually are a consequence of applying first principles to a technical or physical problem at hand. That means, that most of the time the physics also have to be taken into account especially for validation of the numerical solution obtained. This book aims especially at engineers and scientists who have ’real world’ problems. It will concern itself less with pesky mathematical detail. For the interested reader though, we have included sections on mathematical theory to provide the necessary mathematical background. Since this treatment had to be on the superficial side we have provided further reference to the literature where necessary.13332023-02-06T20:42:48Z2024-02-26T14:36:16ZΥπολογιστική Φυσική: Ένα βιβλίο του Κωνσταντίνου Αναγνωστόπουλου - δεύτερη έκδοση<img alt="Read more about Υπολογιστική Φυσική: Ένα βιβλίο του Κωνσταντίνου Αναγνωστόπουλου - δεύτερη έκδοση" title="Υπολογιστική Φυσική: Ένα βιβλίο του Κωνσταντίνου Αναγνωστόπουλου - δεύτερη έκδοση cover image" class="cover " width="353" height="500" data-controller="cover" data-placeholder="/assets/common/placeholder-0e0607cbc50663ddb9e8fd188058bcd2630c730ef6ee322801278607b7d5af8e.png" src="/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsiZGF0YSI6NDUyMywicHVyIjoiYmxvYl9pZCJ9fQ==--83752a9a9a4bb322cdb51b3774837e205ecc0abf/00_master_document_WithCover.pdf.jpg" />Το βιβλίο αυτό είναι μια εισαγωγή στις υπολογιστικές μεθόδους που χρησιμοποιούνται στη φυσική και άλλα επιστημονικά πεδία. Απευθύνεται σε κοινό που έχει ήδη εκτεθεί σε μαθήματα γενικής φυσικής που διδάσκονται στα δύο πρώτα έτη πανεπιστημιακών τμημάτων θετικών επιστημών και επιστημών του μηχανικού. Δεν υποθέτει κανένα υπόβαθρο αριθμητικής ανάλυσης, προγραμματισμού ή χρήσης υπολογιστή και παρουσιάζει ό,τι είναι απαραίτητο για την επίλυση των προβλημάτων που παρουσιάζονται στο βιβλίο. Μπορεί να χρησιμοποιηθεί ως κύριο σύγγραμμα σε εισαγωγικά μαθήματα υπολογιστικής φυσικής και επιστημονικού προγραμματισμού. Το βιβλίο ξεκινάει με πολύ απλά προβλήματα πάνω στην κίνηση σωματιδίου και ολοκληρώνεται με μία εις βάθος συζήτηση προχωρημένων μεθόδων προσομοίωσης Μόντε Κάρλο στη στατιστική φυσική. Το επίπεδο διδασκαλίας είναι διαβαθμισμένο ως προς τη δυσκολία του, παρουσιάζοντας προβλήματα όπως η εξίσωση διάχυσης, δυναμικά συστήματα, ηλεκτροστατική στο επίπεδο, κβαντομηχανική και οι τυχαίες διαδρομές. Όλο το υλικό που παρουσιάζεται στο βιβλίο μπορεί να διαδαχθεί άνετα σε δύο εξάμηνα. Με κατάλληλη επιλογή θεμάτων, μπορεί να χρησιμοποιηθεί και σε ένα πλήρες μάθημα ενός εξαμήνου. Το βιβλίο αποσκοπεί να δώσει στους φοιτητές το υπόβαθρο και την εμπειρία που απαιτείται ώστε να προχωρήσουν σε προγράμματα υπολογισμών υψηλής απόδοσης. Δίνει έμφαση στον εμπειρικό προγραμματισμό αριθμητικών προγραμμάτων, αλλά και στην παραγωγή, ανάλυση και φυσική ερμηνεία των δεδομένων. Επίσης γίνεται προσπάθεια να διατηρηθεί το ενδιαφέρον του αναγνώστη μέσα από την παρουσίαση της αριθμητικής επίλυσης γνωστών και ενδιαφέροντων προβλημάτων φυσικής, οπως χαοτικών συστημάτων, κβαντομηχανικής, ειδικής θεωρίας της σχετικότητας και της φυσικής των μεταβάσεων φάσης. Το βιβλίο και το συνοδευτικό λογισμικό δίνονται ελεύθερα, αδειοδοτούμενα υπό Creative Commons License/GNU public License.13312023-02-06T18:53:04Z2024-01-22T14:52:38ZCOMPUTATIONAL PHYSICS: A Practical Introduction to Computational Physics and Scientific Computing (using C++) - Second Edition<img alt="Read more about COMPUTATIONAL PHYSICS: A Practical Introduction to Computational Physics and Scientific Computing (using C++) - Second Edition" title="COMPUTATIONAL PHYSICS: A Practical Introduction to Computational Physics and Scientific Computing (using C++) - Second Edition cover image" class="cover " width="506" height="586" data-controller="cover" data-placeholder="/assets/common/placeholder-0e0607cbc50663ddb9e8fd188058bcd2630c730ef6ee322801278607b7d5af8e.png" src="/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsiZGF0YSI6NDUxOSwicHVyIjoiYmxvYl9pZCJ9fQ==--007fa9664f3746250b6dfcccfd6b659837e870c0/Screen%20Shot%202023-02-06%20at%2012.48.12%20PM.png" />This book is an introduction to the computational methods used in physics, but also in other scientific fields. It is addressed to an audience that has already been exposed to the introductory level of college physics, usually taught during the first two years of an undergraduate program in science and engineering. It assumes no prior knowledge of numerical analysis, programming or computers and teaches whatever is necessary for the solution of the problems addressed in the text. It can be used as a textbook in introductory computational physics or scientific computing classes. The book starts with very simple problems in particle motion and ends with an in-depth discussion of advanced techniques used in Monte Carlo simulations in statistical mechanics. The level of instruction rises slowly, while discussing problems like the diffusion equation, electrostatics on the plane, quantum mechanics and random walks. All the material can be taught in two semesters, but a selection of topics can form the material of a one semester course. The book aims to provide the students with the background and the experience needed in order to advance to high performance computing projects in science and engineering. It puts emphasis on hands--on programming of numerical code but also on the production, analysis and interpretation of data. But it also tries to keep the students motivated by considering interesting applications in physics, like chaos, quantum mechanics, special relativity and the physics of phase transitions. There is a C++ and a Fortran edition for the core programming. Data analysis is performed using the powerful tools of the GNU/Linux environment. All the necessary software is open source and freely available. The book and the accompanying software are given under a Creative Commons License/GNU public License as a service to the community. It can be used freely as a whole, or any part of it, in any form, by anyone. There is no official distribution of hard copies, but you can use the printing service of your preference in order produce any number of copies you need for you and/or your students. For the lazy ones, a very nice and cheap paperback can be purchaced from lulu.com, amazon.com and conventional bookstores. The ebook can be read in most electronic devices like your PC, tablet or favorite ebook reader and it is freely available from the book's website.12432022-09-01T17:40:40Z2024-01-22T14:52:34ZThe Art of Polynomial Interpolation<img alt="Read more about The Art of Polynomial Interpolation" title="The Art of Polynomial Interpolation cover image" class="cover " width="768" height="1024" data-controller="cover" data-placeholder="/assets/common/placeholder-0e0607cbc50663ddb9e8fd188058bcd2630c730ef6ee322801278607b7d5af8e.png" src="/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsiZGF0YSI6Mzk2OSwicHVyIjoiYmxvYl9pZCJ9fQ==--9f6157517a122f2bdea622d3b3957af7155069b0/The-Art-of-Polynomial-Interpolation_SMurphy_Cover-2-1-768x1024.png" />The inspiration for this text grew out of a simple question that emerged over a number of years of teaching math to Middle School, High School and College students. Practically speaking, what is the origin of a particular polynomial? So much time is spent analyzing, factoring, simplifying and graphing polynomials that it is easy to lose sight of the fact that polynomials have a wealth of practical uses. Exploring the techniques of interpolating data allows us to view the development and birth of a polynomial. This text is focused on laying a foundation for understanding and applying several common forms of polynomial interpolation. The principal goals of the text are: Breakdown the process of developing polynomials to demonstrate and give the student a feel for the process and meaning of developing estimates of the trend (s) a collection of data may represent. Introduce basic matrix algebra to assist students with understanding the process without getting bogged down in purely manual calculations. Some manual calculations have been included, however, to assist with understanding the concept. Assist students in building a basic foundation allowing them to add additional techniques, of which there are many, not covered in this text.12262022-08-16T20:33:22Z2024-01-22T14:52:33ZMathematics for Biomedical Physics<img alt="Read more about Mathematics for Biomedical Physics" title="Mathematics for Biomedical Physics cover image" class="cover " width="116" height="150" data-controller="cover" data-placeholder="/assets/common/placeholder-0e0607cbc50663ddb9e8fd188058bcd2630c730ef6ee322801278607b7d5af8e.png" src="/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsiZGF0YSI6MzkwOSwicHVyIjoiYmxvYl9pZCJ9fQ==--6c936723780bd0a1ae33184b5416f1711d59ad41/thumbnail.jpeg" />Mathematics for Biomedical Physics is an open access peer-reviewed textbook geared to introduce several mathematical topics at the rudimentary level so that students can appreciate the applications of mathematics to the interdisciplinary field of biomedical physics. Most of the topics are presented in their simplest but rigorous form so that students can easily understand the advanced form of these topics when the need arises. Several end-of-chapter problems and chapter examples relate the applications of mathematics to biomedical physics. After mastering the topics of this book, students would be ready to embark on quantitative thinking in various topics of biology and medicine11532022-04-08T03:42:27Z2024-02-15T23:24:27ZIntroduction to Applied Statistics for Psychology Students<img alt="Read more about Introduction to Applied Statistics for Psychology Students" title="Introduction to Applied Statistics for Psychology Students cover image" class="cover " width="683" height="1024" data-controller="cover" data-placeholder="/assets/common/placeholder-0e0607cbc50663ddb9e8fd188058bcd2630c730ef6ee322801278607b7d5af8e.png" src="/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsiZGF0YSI6MzUwNywicHVyIjoiYmxvYl9pZCJ9fQ==--f34dea28c0200a607c6e5a96704ca08f0ceae80e/cover-683x1024.jpg" />Introduction to Applied Statistics for Psychology Students, by Gordon E. Sarty (Professor, Department of Psychology, University of Saskatchewan) began as a textbook published in PDF format, in various editions between 2014-2017. The book was written to meet the needs of University of Saskatchewan psychology students at the undergraduate (PSY 233, PSY 234) level. In 2019-2020, funding was provided through the Gwenna Moss Centre for Teaching and Learning, along with technical assistance from the Distance Education Unit, to update and adapt this book, making it more widely available in an easy-to-use and more adaptable digital (Pressbooks) format. The update also made revisions so that the book could be published with a license appropriate for open educational resources (OER). OERs are defined as “teaching, learning, and research resources that reside in the public domain or have been released under an intellectual property license that permits their free use and re-purposing by others” (Hewlett Foundation). This textbook and other OERs like it are openly licensed using a Creative Commons license, and are offered in various digital and e-book formats free of charge. Printed editions of this book can be obtained for a nominal fee through the University of Saskatchewan bookstore.11212022-01-08T00:50:22Z2024-01-22T14:52:38ZEvidence-based Software Engineering<img alt="Read more about Evidence-based Software Engineering" title="Evidence-based Software Engineering cover image" class="cover " width="446" height="630" data-controller="cover" data-placeholder="/assets/common/placeholder-0e0607cbc50663ddb9e8fd188058bcd2630c730ef6ee322801278607b7d5af8e.png" src="/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsiZGF0YSI6MzI4MywicHVyIjoiYmxvYl9pZCJ9fQ==--cd1f340cfd8195698a5bccbf4417c2a2445a6003/ESEUR-Cover_630-446.jpg" />This book discusses what is currently known about software engineering, based on an analysis of all the publicly available data. This aim is not as ambitious as it sounds, because there is not a great deal of data publicly available. The intent is to provide material that is useful to professional developers working in industry; until recently researchers in software engineering have been more interested in vanity work, promoted by ego and bluster. The material is organized in two parts, the first covering software engineering and the second the statistics likely to be needed for the analysis of software engineering data.10832021-10-13T15:34:30Z2024-01-22T14:52:28ZMostly Harmless Statistics<img alt="Read more about Mostly Harmless Statistics" title="Mostly Harmless Statistics cover image" class="cover " width="116" height="150" data-controller="cover" data-placeholder="/assets/common/placeholder-0e0607cbc50663ddb9e8fd188058bcd2630c730ef6ee322801278607b7d5af8e.png" src="/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsiZGF0YSI6Mjk1MywicHVyIjoiYmxvYl9pZCJ9fQ==--b4d98b816486248eac7eca7f69e9b93d5ba53f40/thumbnail%20(2).jpg" />This text is for an introductory level probability and statistics course with an intermediate algebra prerequisite. The focus of the text follows the American Statistical Association’s Guidelines for Assessment and Instruction in Statistics Education (GAISE). Software examples provided for Microsoft Excel, TI-84 & TI-89 calculators. A formula packet and pdf version of the text are available on the website http://mostlyharmlessstatistics.com. Students new to probability and statistics are sure to benefit from this fully ADA accessible and relevant textbook. The examples resonate with everyday life, the text is approachable, and has a conversational tone to provide an inclusive and easy to read format for students.10782021-09-29T19:27:34Z2024-02-24T00:40:05ZIntermediate Statistics with R<img alt="Read more about Intermediate Statistics with R" title="Intermediate Statistics with R cover image" class="cover " width="609" height="680" data-controller="cover" data-placeholder="/assets/common/placeholder-0e0607cbc50663ddb9e8fd188058bcd2630c730ef6ee322801278607b7d5af8e.png" src="/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsiZGF0YSI6MjkxMywicHVyIjoiYmxvYl9pZCJ9fQ==--01fda43780119fd3655d77aa80da2b4fcbe92c79/Capture.JPG" />Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. This text covers more advanced graphical summaries, One-Way ANOVA with pair-wise comparisons, Two-Way ANOVA, Chi-square testing, and simple and multiple linear regression models. Models with interactions are discussed in the Two-Way ANOVA and multiple linear regression setting with categorical explanatory variables. Randomization-based inferences are used to introduce new parametric distributions and to enhance understanding of what evidence against the null hypothesis “looks like”. Throughout, the use of the statistical software R via Rstudio is emphasized with all useful code and data sets provided within the text. This is Version 3.0 of the book.
https://open.umn.edu/opentextbooks/subjects/applied?page=2