Articles, Case Studies, and Docs

Stop running scripts in isolation. Learn how to transform your local Python code into professional, cloud-based engineering assets using engicloud.ai. By leveraging our AI assistant and modular calculator shells, you can turn static logic into dynamic, shareable workflows in just a few simple steps.

This walkthrough shows you how to use the engicloud.ai "Paper to Equation" tool to transform complex scientific PDFs into functional Python calculators in minutes. From automated extraction and dimensional validation to full documentation, discover how our AI-driven workflow ensures your engineering simulations are accurate, validated, and ready for use.

Learn how engicloud.ai uses semantic search to map your engineering problems to the exact model you need, even if you don't know its name. Discover how to navigate our library of 20,000+ validated calculators using plain language.

Discover how we systematically extracted scattered knowledge from openly available resources and organized it into a library of ~20.000 ready-to use interoperable engineering models.

How to build complex aerospace engineering applications with engicloud.ai

Compare engicloud.ai and MATLAB Simulink across usability, collaboration, openness, AI-assisted modeling, and modern engineering workflows.

Discover how engicloud.ai helps engineers and scientists build, understand, and share models faster with AI, Python, and cloud collaboration.

A steep change in how we preserve, share, and execute engineering knowledge

How a COVID reinfection predictor can become a tool anyone on your team can use—no coding required.

This article shows how to achieve reproducibility, shared assets and ease of use.

Bridging the gap between expert Python packages and collaborative engineering & research

From PDF to executable model: A pharma case study from research we did with the Enabling Technologies Consortium (ETC)

Bring your legacy data and workflows into the modern era, combining the best of physics and AI.

This post examines the current state of LLM reasoning through a scientific lens, drawing on expert perspectives and concrete examples from engineering applications.