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Discover how to import and export Functional Mockup Units (FMUs) with JuliaSim, enabling cross-toolchain workflows and seamless integration of models across different simulation environments.
Discover how to manipulate and visualize data in Julia using Tidier.jl and native Julia functions, converting R data-wrangling techniques.
Discover how to set up the Julia IDE to run R notebooks, creating a powerful multi-language computational environment that surpasses RStudio's capabilities.
Learn how to run R code in Julia using RCall.jl, set up the Julia IDE, convert variables between R and Julia runtimes, and execute R code from the Julia REPL.
Discover how to display R-created plots in Julia with additional tricks and techniques for converting R plotting code.
Discover how to run your existing R markdown notebooks directly on JuliaHub's cloud compute platform using a fully functional R Studio, without making any changes.
Discover how Julia's compiler advancements enable direct binary compilation for real-time model-based applications, eliminating C code dependencies while maintaining high performance across scientific computing and embedded systems.
Discover how to develop acausal models of suspension systems and automatically tune PID controllers using JuliaSim's cloud platform, combining graphical modeling with in-depth coding capabilities.
Master design patterns and deployment strategies for scaling Julia applications efficiently on cloud infrastructure with JuliaHub's enterprise solutions.
Discover how JuliaHub enables secure Julia development with Semgrep-powered static code analysis, compliance guidelines, and automated scanning for regulated industries.
Discover Julia's high-performance capabilities and ModelingToolkit.jl for symbolic, component-based modeling in engineering and scientific computing applications.
Discover how to streamline satellite power analysis using Julia's unified framework, combining orbit modeling and solar panel simulation for faster, reproducible mission design workflows.
Discover how to streamline machine learning development by implementing CI/CD principles using Julia's ecosystem, with practical insights into mass experimentation, model validation, and scalable production workflows.
Discover how JuliaSim accelerates physical product development through advanced modeling, optimization, and simulation capabilities, featuring ModelingToolkit.jl and innovative solver improvements.
Master large-scale compute deployment in Julia, from structuring multi-core computations to building interactive web applications and dashboards with real-time monitoring capabilities.
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