Introduction To The Tools Of Scientific Computi...
The book provides an introduction to common programming tools and methods in numerical mathematics and scientific computing. Unlike standard approaches, it does not focus on any specific language, but aims to explain the underlying ideas.
Introduction to the Tools of Scientific Computi...
At a more advanced level, special tools for the automated solution of partial differential equations using the finite element method are discussed. On a more experimental level, the basic methods of scientific machine learning in artificial neural networks are explained and illustrated.
The book provides an introduction to common programming tools and methods in numerical mathematics and scientific computing. Unlike widely used standard approaches, it does not focus on any particular language but aims to explain the key underlying concepts.
Python is a modern, object-oriented programming language, which hasbecome popular in several areas of software development. This coursediscusses how Python can be utilized in scientific computing. Thecourse starts by introducing some of the main Python tools forcomputing: Jupyter for interactive analysis, NumPy and SciPy fornumerical analysis, matplotlib for visualization, and so on. Inaddition, it talks about how python is used:related scientific libraries, reproducibility, and the broaderecosystem of science in Python, because your work is more than the rawcode you write.
SC 3250 Scientific Computing Toolbox. Team taught course with topics illustrating use of computational tools in multiple science and engineering domains. Topics may include simulations of complex physical, biological, social, and engineering systems, optimization and evaluation of simulation models, Monte Carlo methods, scientific visualization, high performance computing, or data mining.
SC 3850 Independent Study in Scientific Computing. Development of a research project by the individual student under direction of a faculty sponsor. Project must combine scientific computing tools and techniques with a substantive scientific or engineering problem. Consent of both the faculty sponsor and one Director of the SC minor is required.
SC 5250 Scientific Computing Toolbox. Team taught course with topics illustrating use of computational tools in multiple science and engineering domains. Topics may include simulations of complex physical, biological, social, and engineering systems, optimization and evaluation of simulation models, Monte Carlo methods, scientific visualization, high performance computing, or data mining.
Approved courses by subject area are listed below. These courses either provide a detailed treatment of a core scientific computing tool and technique or combine scientific computing tools and techniques with a substantive area of science of engineering. New courses can be approved by the Directors of the minor.
MATH 4630 Nonlinear Optimization. An introduction to modeling, theory and methods for nonlinear optimization problems. Modeling of application problems in science and engineering. Methods of unconstrained optimization with one and several variables. Theory of constrained optimization, including Karush-Kuhn-Tucker conditions. Penalty functions and other methods of constrained optimization. Computer tools such as a subroutine library or symbolic algebra system.
Scientific computing, including modeling, simulation and artificial intelligence, coupled with traditional theoretical and experimental approaches, enables breakthrough scientific discoveries and pushes innovation forward. As scientific modeling and simulation become more complex and ambitious, high-performance computing (HPC), commonly known as supercomputing, provides the invaluable ability to perform these complex calculations at high speeds. Supercomputers along with advances in software, algorithms, methods, tools and workflows equip researchers with powerful tools needed to study systems that would otherwise be impractical, or impossible, to investigate by traditional means due to their complexity or the danger they pose.
The program also includes additional material presented to helpsupport the project activities, such asan overview of the mathematical typesetting system LaTeXand other tools for presentations and report writing,a discussion of academic integrity in scientific work,and a GRE preparation course.All material of this program is designed to include anintroduction that assumes very little background in itbut also very advanced material that will make it useful for experienced users.These broader aspects of the program are designed to make theproject work more effective as well as to provide an excellentpreparation for and impression of graduate studies inmathematics or statistics.
In total, the program of this REU Site, as summarized in thedetailed scheduleprovides a combination of formal introduction tohigh performance computing in the mathematical sciencescovering aspects of scientific, statistical, and parallel computingwith team work on an interdisciplinary application project.This combination of aspects will give participants a powerfuland exciting experience of how to combine learning with applyingmaterial to project work, all in an atmosphere of mutual supportby all members of the project from undergraduate students,graduate students, faculty, to clients.
The unit will enable students to effectively utilise widely used tools in Data Science by introducing them to the fundamentals of scientific computing. Such tools include, but are not limited to, Linux shell and command line usage, GitHub and version control, SciPy, basic programming with Python for spatial applications, use and creation of metadata, parallelising code. This is a technical unit and the emphasis will be on how to use these tools using a variety of applications both from physical and human domains.
Summative: Two 2,000-word reports (each worth 50% of the unit mark) describing the use of different scientific computing tools (e.g. linux and HPC, GitHub and version control, SciPy and basic programming with Python, parallelising code). The reports will be written in a reproducible manner and will include the necessary code.
High performance computing (HPC) is one of the most essential tools fueling the advancement of computational science. And the universe of scientific computing has expanded in all directions. From weather forecasting and energy exploration, to computational fluid dynamics and life sciences, researchers are fusing traditional simulations with artificial intelligence, machine learning, deep learning, big data analytics, and edge-computing to solve the mysteries of the world around us.
This introduction to the field of scientific computing will focus on using mathematical software and programming as tools in mathematical modeling and problem solving. Motivated by various types of mathematical models (discrete, continuous, deterministic, stochastic, etc.), we will investigate software options that are best suited for implementing our models and simulations. Prerequisite: MATH 255. Three credit hours.
Created to help scientists and engineers write computer code, this practical book addresses the important tools and techniques that are necessary for scientific computing, but which are not yet commonplace in science and engineering curricula. This book contains chapters summarizing the most important topics that computational researchers need to know about. It leverages the viewpoints of passionate experts involved with scientific computing courses around the globe and aims to be a starting point for new computational scientists and a reference for the experienced. Each contributed chapter focuses on a specific tool or skill, providing the content needed to provide a working knowledge of the topic in about one day. While many individual books on specific computing topics exist, none is explicitly focused on getting technical professionals and students up and running immediately across a variety of computational areas.
An introduction to paradigms and languages used in internet and World Wide Web programming. These include modern tools for client-side and server-side programming and dynamic Web page generation. Advanced topics, such as security and XML, will be covered as time allows. Public Affairs Capstone Experience course.
An introduction to software and techniques used in data science. Topics will include sources of data, data preparation, data analysis, use of software tools, development of data analysis software, and ethical and legal considerations.
HKHLR & CSC offers, every second month, a course session for researchers using, or interested to use, the Goethe-CSC or FUCHS in Frankfurt. The course sessions gives a cluster computing introduction, are hosted at the Goethe university in Frankfurt am Main and are delivered by HPC experts. The sessions are readily cluster-oriented, so knowledge of a programming language or of a scientific computing software is assumed. Training session are delivered in English.You can download a quick reference guide with a brief instruction how to work on the Goethe-CSC cluster?, facts about the cluster and a short reference of SLURM commands. 041b061a72