Introduction to the Tools of Scientific Computing
Smith, Einar
Sold by Buchpark, Trebbin, Germany
AbeBooks Seller since September 30, 2021
Used - Hardcover
Condition: Used - Fine
Ships from Germany to U.S.A.
Quantity: 1 available
Add to basketSold by Buchpark, Trebbin, Germany
AbeBooks Seller since September 30, 2021
Condition: Used - Fine
Quantity: 1 available
Add to basketZustand: Sehr gut | Seiten: 444 | Sprache: Englisch | Produktart: Bücher | 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.Typically, new concepts are first introduced in the particularly user-friendly Python language and then transferred and extended in various programming environments from C/C++, Julia and MATLAB to Maple and Mathematica. This includes various approaches to distributed computing. By examining and comparing different languages, the book is also helpful for mathematicians and practitioners in deciding which programming language to use for which purposes.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.
Seller Inventory # 40749648/2
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.
Typically, new concepts are first introduced in the particularly user-friendly Python language and then transferred and extended in various programming environments from C/C++, Julia and MATLAB to Maple and Mathematica. This includes various approaches to distributed computing. By examining and comparing different languages, the book is also helpful for mathematicians and practitioners in deciding which programming language to use for which purposes.
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.
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