Individual skill
physicsnemo-discover
Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML/AI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse, generative). Points at existing files via live repo search; never writes code. Do NOT use for installation or environment setup, training-loop or other code authoring/scaffolding, contributor/CI/packaging questions, repo-specific questions in physicsnemo-sym/-cfd/-curator, or general (non-physics) ML/PyTorch.
skills/physicsnemo-discover/SKILL.md
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- Last checked
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- Repository last updated
- Nov 6, 2026
- License
- Apache-2.0
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BENCHMARK.mddoc · 4,290 bytesSKILL.mdcore · 7,464 bytesevals/evals.jsonasset · 4,253 bytesreferences/RECIPES.mddoc · 7,121 bytesreferences/TAXONOMY.mddoc · 12,071 bytesskill-card.mddoc · 3,818 bytesskill.oms.sigasset · 5,061 bytes
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# PhysicsNeMo Discoverability
Help a user navigate PhysicsNeMo: point them at files, folders, examples, and docs **in the repo at its current state**. Never write training code; never cite a path from memory.
## Core principle
PhysicsNeMo evolves — classes get renamed, examples move, `experimental/` graduates. Any static list of class names and paths rots, so **discover, don't remember**: enumerate from the live repo every turn.
PhysicsNeMo is **composable**: each solution is a product (model family × datapipe × training strategy × config). An example is one reference instantiation of that product, not a prescription. Surface the **axes** and the **menu along each axis**, then cite examples as concrete starting points to fork and recombine.
## What a correct answer satisfies
These are constraints, not a script — choose the searches that meet them and skip work the task doesn't need. Search patterns per axis live in `references/RECIPES.md`.
