By: Deep Datta

Re-posted from: https://info.juliahub.com/blog/create-low-code-apps-on-juliahub

Users of JuliaHub are already familiar with the Genie framework,  a full-stack web framework that offers a streamlined and efficient workflow for developing modern web applications. Building on Julia’s strengths such as high-level, high-performance, dynamic nature, and Just-Time-Compilation (JIT), Genie offers a wealth of features through its rich API and robust toolset for web development. 

By: Deep Datta

Re-posted from: https://info.juliahub.com/blog/julia-security-and-semgrep-static-code-analysis

The Julia programming language is widely considered a powerful and versatile tool for modern technical computing. Combining the high-level syntax and dynamism of languages like Python with the performance capabilities of languages such as C and Fortran, Julia is the go-to choice for a diverse range of computational tasks. Julia’s popularity and appeal lie in the elegant syntax, exceptional speed, and scalability that make it suitable for building computationally intensive tasks or everyday programming needs. 

By: Chris Rackauckas

Re-posted from: https://info.juliahub.com/blog/digital-echo-industrial-surrogate-modeling

Industrial surrogate modeling is typically centered around neural network architectures and precision. While important, these approaches don’t necessarily solve the ‘surrogates problem’. The ‘surrogates problem’ refers to the challenges associated with integrating surrogate models into industrial processes and requirements infrastructure. The challenge with this approach is that it doesn’t integrate surrogates into industrial processes and requirements infrastructure.  For surrogate modeling to truly solve real-world industrial engineering problems, verification and validation benchmarks are equally important. This blog post looks at the three key pillars to create surrogates that are process-driven, reliable, and prioritize prediction time for varied industrial applications.