ASP Artificial Scientific Programming

Artificial scientific programming language for solving differential equation

Scientific Programming Tools for Water Management

This special issue delivers a platform in which researchers expose intersections between algorithm design, software platforms, and hardware architectures to deal with emerging challenges in the scientific field of management of water and water-dependent resources. Since the call for papers was announced in June 2019, this special issue has received 10 manuscripts. After a rigorous review process, 6 papers have been finally accepted for publication. Published papers deal with groundwater quality monitoring, coastal groundwater-dependent irrigation agriculture, desertification risk, water recovery from tailings, future scenarios of water resources, and vulnerability of coastal aquifers

Data-Driven Computational Intelligence for Scientific Programming

Rubio-Largo, Á., Preciado, J. C., & Iribarne, L. (2019). Data-Driven Computational Intelligence for Scientific Programming. Scientific Programming,[5235706].[Editorial]. Doi: https://doi.org/10.1155/2019/5235706publishersversionpublishe

Scientific Programming and Computer Architecture

A variety of programming models relevant to scientists explained, with an emphasis on how programming constructs map to parts of the computer.What makes computer programs fast or slow? To answer this question, we have to get behind the abstractions of programming languages and look at how a computer really works. This book examines and explains a variety of scientific programming models (programming models relevant to scientists) with an emphasis on how programming constructs map to different parts of the computer's architecture. Two themes emerge: program speed and program modularity. Throughout this book, the premise is to "get under the hood," and the discussion is tied to specific programs. The book digs into linkers, compilers, operating systems, and computer architecture to understand how the different parts of the computer interact with programs. It begins with a review of C/C++ and explanations of how libraries, linkers, and Makefiles work. Programming models covered include Pthreads, OpenMP, MPI, TCP/IP, and CUDA.The emphasis on how computers work leads the reader into computer architecture and occasionally into the operating system kernel. The operating system studied is Linux, the preferred platform for scientific computing. Linux is also open source, which allows users to peer into its inner workings. A brief appendix provides a useful table of machines used to time programs. The book's website (https://github.com/divakarvi/bk-spca) has all the programs described in the book as well as a link to the html text

Introduction to Scientific Programming with Python

This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies