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.
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.
Introduction to the Tools of Scientific Computing
428
Introduction to the Tools of Scientific Computing
428Hardcover(Second Edition 2022)
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Product Details
| ISBN-13: | 9783031169717 |
|---|---|
| Publisher: | Springer International Publishing |
| Publication date: | 10/28/2022 |
| Series: | Texts in Computational Science and Engineering , #25 |
| Edition description: | Second Edition 2022 |
| Pages: | 428 |
| Product dimensions: | 6.10(w) x 9.25(h) x (d) |