Julia Lang - Enabling Mathematical Modeling
In 2016, I started using Julia Lang based on its open source availability and the extensive published conference videos posted on YouTube. I liked Julia for several reasons: (1) linking to C, C++, FORTRAN, Python libraries, (2) GPU interface support, (3) REPL, (4) parallel compute interfaces and (5) differential equations. Differential equations are at the core of most domains, enabling compact mathematical equations relating subsystems to each other. There are many programming language utilized for a variety of tasks including web services, databases, operating systems, distributed systems, file systems, etc. There are very few programming languages dedicated to ease scientific and engineering modeling and exploration. With ease means that first level programming entities such as matrices and vectors. Famous proprietary examples include Mathematica, Macsyma, Matlab. Julia provides a shareable, non-proprietary implementation: https://juliapackages.com/p/diffeqtut...