Category Archives: Julia

Julia and MATLAB can coexist. Let us show you how.

By: Great Lakes Consulting

Re-posted from: https://blog.glcs.io/juliacon-2025-preview

This post was written by Steven Whitaker.

Have you ever wished you could start using the Julia programming languageto develop custom models?Does the idea of replacingoutdated MATLAB code and modelsseem overwhelming?

Or maybe you don’t plan to replace all MATLAB code,but wouldn’t it be excitingto integrate Julia codeinto existing workflows?

Also, technicalities aside,how do you convince your colleaguesto make the leapinto the Julia ecosystem?

I’m excited to sharean announcement!At this year’s JuliaCon,I will be speaking abouta small but significant stepyou can take to start adding Juliato your MATLAB codebase.

Great news!You can transition to Julia smoothlywithout completely abandoning MATLAB.There’s a straightforward methodto embrace the best of both worlds,so you won’t needto rewrite your legacy models from scratch.

I’ll give my full talk in July,but if you don’t want to wait,keep readingfor a sneak peek!

Background

The GLCS.io teamhas been developing Julia-based solutions since 2015.Over the past 4 years,we’ve had the pleasure of redesigning and enhancing Julia modelsfor our clients in the finance, science, and engineering sectors.Its incredible speed and versatility have transformedhow we tackle complex computations together.However,we also fully acknowledge the reality:MATLAB continues to hold a significant placein countless companies and research labs worldwide.

For decades,MATLAB has been the benchmarkfor data analysis, modeling, and simulationacross scientific and engineering fields.There are likely hundreds of thousands of MATLAB licenses in use,with millions of userssupporting an unimaginable number of models and codebases.

Even for a single company,fully transitioning to Juliaoften feels insurmountable.The vast amount of existing MATLAB codepresents a significant challenge for any team considering adopting Julia.

Yet, unlocking Julia’s power is vital for companiesaiming to excel in today’s competitive landscape.The question isn’t if companiesshould adopt Julia—it’s how to do it.

Companies should blend Juliawith their MATLAB environments,ensuring minimal disruption and optimal resource use.This strategic integrationdelivers meaningful gainsin accuracy, performance, and scalabilityto transform operations and drive success.

JuliaCon Preview

At JuliaCon,I’m excited to share how youcan seamlessly integrate Juliainto existing MATLAB workflows—a processthat has delivered up to 100x performance improvementswhile enhancing code quality and functionality.Through a real-world model,I’ll highlight design patterns,benchmark comparisons,and valuable business case insightsto demonstrate the transformative potential of integrating Julia.

(Spoiler alert:the performance improvement is more than 100xfor the example I will show at JuliaCon.)

What We Offer

Unlock high-performance modeling!Our dedicated team is hereto integrate Julia into your MATLAB workflows.Experience a strategic, step-by-step process tailoredfor seamless Julia-MATLAB integration,focused on efficiency and delivering measurable results:

  1. Tailored Assessment:Pinpoint challenges and opportunities for Julia to address.
  2. MATLAB Benchmarking:Establish a performance baseline to measure progress and impact.
  3. Julia Model Development:Convert MATLAB models to Juliaor assist your team in doing so.
  4. Julia Integration:Combine Julia’s capabilities with your existing MATLAB workflows for optimal results.
  5. Roadmap Alignment:Validate performance improvements,create a strong business case for leadership,and agree on future support and innovation.

Check out our website for more details.

Summary

By attending my JuliaCon talk,you will learnhow to seamlessly integrate Juliainto your existing MATLAB codebase.And by leveraging our support at GLCS,you can adopt Juliawithout disruption—unlocking faster computations,improved models,and better scalabilitywhile retaining the strengthsof your MATLAB codebase.

Are you or someone you knowexcited about harnessing the power of Julia and MATLAB together?Let’s connect! Schedule a consultation todayto discover incredible performance gains of 100x or more.

Additional Links

MATLAB is a registered trademarkof The MathWorks, Inc.

Cover image:The JuliaCon 2025 logowas obtained from https://juliacon.org/2025/.

]]>

The Numerical Analysis of Differentiable Simulation: Automatic Differentiation Can Be Incorrect

By: Christopher Rackauckas

Re-posted from: https://www.stochasticlifestyle.com/the-numerical-analysis-of-differentiable-simulation-automatic-differentiation-can-be-incorrect/

ISCL Seminar Series

The Numerical Analysis of Differentiable Simulation: How Automatic Differentiation of Physics Can Give Incorrect Derivatives

Scientific machine learning (SciML) relies heavily on automatic differentiation (AD), the process of constructing gradients which include machine learning integrated into mechanistic models for the purpose of gradient-based optimization. While these differentiable programming approaches pitch an idea of “simply put the simulator into a loss function and use AD”, it turns out there are a lot more subtle details to consider in practice. In this talk we will dive into the numerical analysis of differentiable simulation and ask the question: how numerically stable and robust is AD? We will use examples from the Python-based Jax (diffrax) and PyTorch (torchdiffeq) libraries in order to demonstrate how canonical formulations of AD and adjoint methods can give inaccurate gradients in the context of ODEs and PDEs. We demonstrate cases where the methodologies are “mathematically correct”, but due to the intricacies of numerical error propagation, their approaches can give 60% and greater error even in simple cases like linear ODEs. We’ll then describe some of the non-standard modifications to AD which are done in the Julia SciML libraries to overcome these numerical instabilities and achieve accurate results, crucially also describing the engineering trade-offs which are required to be made in the process. The audience should leave with a greater appreciation of the greater numerical challenges which still need to be addressed in the field of AD for SciML.

The post The Numerical Analysis of Differentiable Simulation: Automatic Differentiation Can Be Incorrect appeared first on Stochastic Lifestyle.

JuliaHub Audit Features for Pharmacometrics Software Records

By: Mridul Upadhyay

Re-posted from: https://info.juliahub.com/blog/juliahub-audit-features

JuliaHub traceability dashboard for audit and compliance insights

Ensuring Reproducibility and Compliance through Robust Auditability and Traceability

In the dynamic realm of scientific computing and data analysis, the importance of maintaining the integrity of every artifact and computational process cannot be overstated. Meticulous tracking and documentation of every action on data and code are crucial for valid research and regulatory compliance. JuliaHub provides robust audit trail and traceability features to meet these demands.

The Foundation of Trust: Audit Trails and Traceability

Audit trails are secure, time-stamped records detailing who did what, when, and often where. They are vital for accountability, issue diagnosis, and system validation, especially for regulatory compliance. Traceability extends this by tracking data and artifact lineage from origin to final use, ensuring data accuracy, governance, security, and regulatory adherence. JuliaHub provides comprehensive audit logs which include user actions, controls access, and tracks data and code provenance, crucial for regulated and collaborative research.

Audit Log in the JuliaHub Platform

Furthermore, The Traceability Dashboard view gives users each event in a human readable format. The Dashboard allows administrators to view, filter, sort, and download audit events related to compliance, privacy, and data modifications. Events are organized by resource type (datasets, folders, packages, notebooks). For example, a new dataset version triggers an “add_version” event with details. Anytime an artifact is uploaded, modified, deleted, or copied – the event is tracked. Administrators can filter by user or date for targeted audits and download data for further analysis.

The Traceability Dashboard: Your Computational History at a Glance

Traceability in Practice: Tracking Code and Data Lineage

JuliaHub’s traceability features are multifaceted. Git version control in Projects tracks and allows rollback of changes. The package manager records dependencies in a manifest file. Private Git repositories are supported. The Traceability Dashboard tracks artifact lineage. The Time Capsule feature records batch jobs for reproducibility, and the Data API offers secure, versioned, auditable storage.

More Features of the JuliaHub Platform:

Granular Access Control: Securing Your Research Environment

JuliaHub implements strong access controls. The Projects feature facilitates team collaboration with role-based access (Viewer, Editor, Owner). Integration with organizational identity providers and support for individual users ensure secure access to resources like datasets and private Julia package registries.  

Compliance and Security: Core Principles of JuliaHub

JuliaHub prioritizes security and compliance, holding SOC 2 compliance and meeting standards like FDA 21 CFR Part 11 and GAMP 5. Detailed audit logs and a Trust and Security Portal provide transparency.

Conclusion: JuliaHub – Your Partner for Reliable Scientific Research

JuliaHub’s audit and traceability features offer significant benefits in regulated and collaborative pharmaceutical environments, streamlining compliance. In conjunction with Pumas-AI, JuliaHub provides a robust set of tools for pharmacometric workflows.

JuliaHub’s comprehensive audit trail and traceability features, including detailed audit logging, access controls, Git versioning, software bill of materials, and Traceability Dashboard view, provide significant records for regulated industries. Its focus on compliance and data integrity, combined with collaborative tools, positions JuliaHub as a strong platform for the pharmaceutical and bio-tech industries as a whole.

To learn more, and try JuliaHub today, visit us and then schedule a free consultation for more information.