NVIDIA Modulus
TOOLOpen-source deep learning framework for physics-informed machine learning (Physics-ML). Build, train, and fine-tune models for AI4science and engineering applications using state-of-the-art SciML methods. Apache 2.0 licensed
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NVIDIA Modulus: a free, copy-ready tool on OpenRuna. Open-source deep learning framework for physics-informed machine learning (Physics-ML). Build, train, and fine-tune models for AI4science and engineering applications usi
What this tool does
"NVIDIA Modulus" is a tool you can copy, adapt, and run with any modern AI assistant. Open-source deep learning framework for physics-informed machine learning (Physics-ML). Build, train, and fine-tune models for AI4science and engineering applications using state-of-the-art SciML methods. Apache 2.0 licensed On OpenRuna it sits inside a connected graph of related tools, tools, and datasets, so branching into adjacent resources is one click away. Open a new conversation and paste it in, wire it into an agent, or keep it in your team's prompt library.
Use cases
- Keep it in a shared library as the canonical version of this tool for your organisation.
- Fork it as a baseline and layer in your own project context, constraints, and examples.
- Hand "NVIDIA Modulus" to a new teammate so their tool output matches your team's quality bar from day one.
- Use "NVIDIA Modulus" when you need a repeatable tool for professional work without rewriting instructions every time.
Example output
Ask the model to apply "NVIDIA Modulus" to your scenario and it returns a structured answer — clear sections, actionable steps, and assumptions stated upfront — ready to paste into docs, tickets, or code comments. Add one example of your own and the output quality jumps noticeably.
Tips by platform
Claude
In Claude, paste the full tool as your first message or add it to Project instructions, then ask Claude to confirm assumptions before it executes. For longer tools, iterate inside the artifact panel.
ChatGPT
For ChatGPT, save this tool as a Custom Instruction or a saved prompt so it is one click away. Add your specifics in a follow-up rather than editing the original.
Cursor
Add this tool to your Cursor rules and invoke it from Agent mode for repeatable results. Link back to its OpenRuna page in the rule so the source stays discoverable.
Frequently asked questions
- What is "NVIDIA Modulus"?
- It is a tool listed on OpenRuna — Open-source deep learning framework for physics-informed machine learning (Physics-ML). Build, train, and fine-tune models for AI4science and engineering applications using state-of-the-art SciML methods. Apache 2.0 licensed You can copy and adapt it for ChatGPT, Claude, Cursor, or any other AI assistant.
- Is "NVIDIA Modulus" free to use?
- Most OpenRuna resources are open or CC0-licensed. Check the license shown on this page before commercial use; premium collections are clearly marked as such.
- How do I get the best results from this tool?
- Replace any placeholders, add your project context, and ask the model to confirm its assumptions first. Iterate over 2–3 follow-up turns rather than expecting a perfect first response.
- Does "NVIDIA Modulus" work with both Claude and ChatGPT?
- Yes — it is model-agnostic text, so it runs on Claude, ChatGPT, Gemini, and Cursor. The tips on this page cover each of those assistants specifically.
- Where can I find resources related to "NVIDIA Modulus"?
- Scroll to the Related resources section on this page, or open the matching category hub on OpenRuna to find connected prompts, tools, agents, and datasets in the same topic area.
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