From FAIR to FIRES: the Next Evolution in Scientific Knowledge

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

The FAIR Foundation and Its Limits

For years, the scientific community has rallied around FAIR principles, making data and models Findable, Accessible, Interoperable, and Reusable. This framework has transformed how research outputs are shared, ensuring that knowledge doesn't disappear into silos.

But FAIR is falling short when it comes to adoption, because it stops at access.

A research paper with supplementary code meets FAIR criteria. A dataset in a repository meets FAIR criteria. Yet both remain fundamentally static, as they can be found and downloaded, but not necessarily readily executed, verified, or built upon without significant effort.

This is the reproducibility crisis in a nutshell: knowledge that exists but doesn't *work*.

FIRES: Making Knowledge Executable

The FIRES framework extends FAIR with two critical additions:

Executability transforms static assets into living computational tools. Instead of downloading code and setting up environments, users run models immediately, with visualization, APIs, and validation built-in.

Synthesizability enables composition: connect models into workflows, abstract complexity while maintaining explainability, and create higher-level capabilities from proven components.

Relevance for Engineering and How engicloud.ai Embodies FIRES

Engineering knowledge has a specific pain point: implementation friction.

A mechanical engineer knows the Darcy-Weisbach equation exists. Finding it takes minutes. Implementing it correctly takes hours. Validating it against known solutions takes days. Multiply this across thousands of equations, and teams spend more time building tools than solving problems.

FAIR makes the equation findable. FIRES makes it work.

engicloud.ai isn't just another equation database. It's built from the ground up on FIRES principles:

Findability

Our semantic search engine indexes ~40,000 engineering equations by name, description, formula similarity, and application domain. Search for "pressure drop in a pipe" using the semantic search in the AI assistant and find Darcy-Weisbach, Hagen-Poiseuille, and Colebrook-White — not by keyword matching, but by understanding what you're trying to calculate.

Interoperability

Every equation ships with a ready-to-use Python implementation — properly typed, documented, and NumPy-compatible. No environment setup. No dependency conflicts. No "it works on my machine.".

Reusability

Models are versioned, attributed, and released under open licenses. When a team member leaves, their contributions remain searchable, documented, and immediately usable by successors. This is knowledge retention, not knowledge loss.

Executability

This is where engicloud diverges from traditional databases:

• Calculators encapsulate Python code into reusable, parameterized tools
• Projects chain calculators into end-to-end workflows
• AI assistance generates new models from PDFs and scientific papers
• Live execution runs models directly in the platform with visualization

The gap between "I found the equation" and "I have an answer" collapses from hours to seconds.

Synthesizability

engicloud.ai enables composition:

• Combine heat transfer + fluid dynamics calculators into a thermal system workflow
• Wrap that workflow into a new calculator for others to use
• Build team libraries of validated workflows that become organizational assets
• Integrate with databases, spreadsheets, and external APIs

Complexity is managed through abstraction layers, but every step remains explainable and auditable, exactly as FIRES requires.

From Crisis to Capability

The reproducibility crisis is about the gap between what researchers know and what they can easily execute.

FAIR made knowledge accessible, now FIRES makes it actionable.
Explore engicloud.ai (https://engicloud.ai) and discover how executable models transform your workflow.

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engicloud.ai: Your link between AI and physics

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