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

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 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: 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.

physicsnemo-discover