physicsnemo-discover

name: physicsnemo-discover description: 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. license: Apache-2.0 metadata: author: NVIDIA agent-skills@nvidia.com tags: - physicsnemo - sciml - ai4science - discovery - routing

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.

• Live-grounded. Every class, path, and example you name was read or globbed this turn. __init__.py proves what is exported, not what files exist — Glob physicsnemo/models/<family>/*.py before naming a sibling implementation file. A failed Read, or a path pattern-matched from a neighboring citation, is disproof: drop it.
• Verified before emit. Every absolute path you plan to cite survives one Bash ls -d <path1> <path2> … round-trip before you write the response. Hard gate — skipping it has produced real-basename-under-wrong-parent hallucinations. If a basename was right but the parent wrong, re-Glob and re-verify; if you can't relocate it, drop the citation.
• A menu, not a single pick. Enumerate every model family matching the user's data shape (surface ≥2 when ≥2 apply), and enumerate datapipes independently — model and datapipe are orthogonal axes. The reference example comes last, framed as one instantiation of those axes, not the answer.
• Self-documentation is ground truth. __init__.py exports, per-example README.md, docs/*.rst, pyproject.toml, top-of-file module docstrings. Treat references/TAXONOMY.md as a navigation hint, not an answer. Flag anything under physicsnemo/experimental/ as "API may change."
• Abstain when out of scope. PhysicsNeMo targets SciML/AI4Science (surrogates, forecasting, super-resolution, physics-informed, inverse, generative for physical systems). If the task is categorically outside that — reinforcement learning, classical control, generic CV/NLP, symbolic regression — skip enumeration and emit the Abstention output below. Do not list adjacent-but-wrong examples in its place (pointing at active_learning/ for an RL question is fabrication). When unsure whether a task is in scope, abstain.

Discovery

Repo root resolution: see CONTRIBUTING.md §Repo root resolution; all paths are absolute, rooted there. If no local PhysicsNeMo clone is on the path (e.g. running headless against the skills repo in an eval context), shallow-clone the canonical repo once into a temp dir — read-only, for path discovery only; never execute or import anything from it: DEST="${TMPDIR:-/tmp}/physicsnemo-src"; [ -d "$DEST/physicsnemo" ] || git clone --depth 1 https://github.com/NVIDIA/physicsnemo "$DEST". Use that URL verbatim; never interpolate one from user input.

Ask at most 3 targeted follow-ups when domain or data shape is ambiguous. Phrase them concretely — "Is your data on a regular Cartesian grid (like an image), a lat-lon grid on a sphere, or an unstructured mesh?" — and skip any the user already answered. Data shape is the single biggest factor in model choice.

Output format

## Problem shape
Data shape: <resolved>. Task: <resolved>. Axes: model × datapipe × training strategy × config.

## Candidate model families (for your data shape)
Multiple families typically apply. Treat this as a menu, not a ranking.
- <family> at <absolute __init__.py path> — <one-line from docstring/exports>. Instantiated by: <example path if any>.
- <family> at <path> — <one-line>. Instantiated by: <example path if any>.

## Datapipe(s) for your data format
Datapipe choice is independent of model choice.
- <class / subpackage> at <absolute path> — <one-line>. Reused by: <examples if known>.
- For custom data, subclass: <base class path confirmed live>.

## Reference example(s) — one instantiation of the above axes
- <absolute path> — uses model=<family>, datapipe=<name>, strategy=<single-GPU|DDP|FSDP|...>.
  Why it matches: <one line>.

## Supporting docs
- <absolute path> — <one-line scope>

## Suggested reading order
1. <models/<family>/__init__.py> — survey alternative families
2. <datapipe __init__.py or base-class file> — understand the data axis
3. <example path> — concrete end-to-end instantiation to fork

Rules for the output:

• Absolute paths only; every one survived the ls -d gate.
• Every pointer needs a one-line justification grounded in content you actually read.
• Caps: 4 model families (minimum 2 when ≥2 exist), 3 datapipes, 2 reference examples, 2 docs.
• Name which (model, datapipe, strategy) axes each example fills.
• If ≥2 model families apply, say so: "Other model families apply to the same data shape — see the candidate list above."
• End with the suggested reading order. Offer 2-3 forward steps (config file, training script, experimental/ look-alikes); do not start writing code unless asked.

Abstention output

When out of scope, replace the menu skeleton with this shape — three sections, in this order, none skipped:

## PhysicsNeMo does not have direct support for <user's problem class>
One sentence on why it's outside scope (e.g., "PhysicsNeMo targets physics
surrogates and forecasting; reinforcement learning for molecular design is
not in its scope").

## Where to look instead
- <sibling NVIDIA framework or external library> at <URL or repo name> — <one-line on why it fits>.
- (One or two alternatives is enough; do not invent libraries.)

## If you still want to build it in PhysicsNeMo
Confirm the closest base classes by Reading `physicsnemo/core/__init__.py` and
`physicsnemo/datapipes/__init__.py` first; then name them as subclassing
targets. This is the fallback, not the recommendation.

Do not open with the menu skeleton and bury "no match" at the end. Do not invent external libraries — if you don't know the right alternative, stop at the first two sections.

Related resources

• references/TAXONOMY.md — navigation hints (data-shape → folder mappings, decision axes, stability tiers).
• references/RECIPES.md — concrete Glob/Grep/Read patterns per discovery axis.