By: Jasmine Chokshi

Re-posted from: https://info.juliahub.com/blog/a-next-gen-approach-to-modeling

ModelingToolkit.jl is a high-performance modeling framework in Julia that integrates symbolic and numeric computations, to meet the needs of scientific computing and scientific machine learning. It combines elements from symbolic computational algebra systems with both causal and acausal equation-based modeling paradigms, offering an extensible and parallelizable environment for complex system modeling.  

By: Sanjeeb Das Gupta

Re-posted from: https://info.juliahub.com/blog/bridging-julia-and-c-with-cxxwrap.jl

The Julia programming language is gaining traction as a powerful tool for modeling and simulation due to its speed, ease of use, and dynamic nature. However, many legacy simulation libraries are written in C++, a language known for its efficiency and extensive ecosystem. The challenge arises when trying to integrate high-performance C++-based simulation tools into Julia workflows.

By: Jasmine Chokshi

Re-posted from: https://info.juliahub.com/blog/c-code-generation-julia

For years, a sort of dogma has prevailed in the world of embedded systems and real-time control: if you’re using a high-level language like Python or R for modeling and simulation, you must rewrite your code in C for deployment. But what if there was a better way? What if you could leverage the productivity of high-level languages without sacrificing performance?