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name: neuromancer description: "Guide NeuroMANCER 1.5.6 differentiable scientific machine-learning workflows for constrained optimization, dynamics modeling, data and training, predictive control, simulation, and structured operators." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

NeuroMANCER

Use this repo skill when a task names NeuroMANCER or asks for PyTorch-based scientific machine learning involving symbolic constraints, differentiable programming, neural ODE/PDE/SDE models, system identification, predictive control, PSL simulation, or structured linear operators.

First route by the user's deliverable

• Formulate a constrained or symbolic problem — read sub-skills/symbolic-problems/SKILL.md. This owns Variable, objectives, constraints, aggregate losses, Node, and Problem graphs.
• Prepare data or train/evaluate a model — read sub-skills/data-training/SKILL.md. This owns dictionary/static/sequence/graph schemas, normalization, splits, collators, Trainer, Lightning, callbacks, and logging.
• Construct a neural dynamical model — read sub-skills/dynamics-modeling/SKILL.md. This owns blocks, activations, ODE/SDE/PINN/DAE components, integrators, interpolation, and system-identification models.
• Roll out a plant or build a control loop — read sub-skills/control-simulation/SKILL.md. This owns System, preview and moving-horizon wiring, PSL simulators, signals, emulators, and bounded DPC/control recipes.
• Choose a structured map or differentiable operator solver — read sub-skills/structured-operators/SKILL.md. This owns pure-Python SLiM maps, structured RNNs, projection/iterative solvers, and dimension restrictions.

For a request that spans routes, establish the data/time/key contract first, then combine the smallest set of sibling routes. Keep symbolic objective and constraint construction separate from model, data, and rollout construction.

Installation and first check

The public distribution is neuromancer==1.5.6 and requires Python >=3.9. Install it in an isolated environment:

python -m pip install neuromancer
python -c "import neuromancer; print(neuromancer.__version__)"

The package declares PyTorch, TorchDiffEq, TorchSDE, Lightning, CVXPY, CVXPYLayers, CasADi, NumPy/SciPy, and plotting dependencies. Install only the optional packages needed by the selected route. PSL imports requests in this release even when package metadata does not list it; if PSL import fails with that missing module, install requests in the isolated target environment.

Run the bundled environment diagnostic before a larger workflow:

python scripts/check_environment.py --help
python scripts/check_environment.py --run

The diagnostic reports package imports, dependency availability, and whether a CUDA device can be probed. A CUDA-enabled PyTorch import is not proof that a NeuroMANCER GPU workflow runs; use CPU as the portable baseline and read the route-specific backend warnings.

Shared operating rules

1. Use module-qualified imports such as neuromancer.constraint, neuromancer.dataset, neuromancer.dynamics.ode, neuromancer.modules.blocks, neuromancer.psl, and neuromancer.slim.
2. Make tensor rank and dictionary keys explicit. Ordinary node calls use (batch, features); rollouts normally use (batch, time, features); sequence datasets emit past/future keys and a collated name.
3. Give nodes, objectives, and constraints unique names. Keep output keys intentional when a graph uses recurrent overwrites.
4. Start with a tiny CPU fixture and assert output shapes, finite values, loss keys, and gradient behavior before adding data, long training, plotting, external files, or GPU execution.
5. Treat network downloads, credentials, large datasets, long experiments, multi-GPU execution, and the native Butterfly factor extension as explicit optional work, not as hidden smoke-test steps.

Read references/installation-and-overview.md for the package mental model and route selection, and references/troubleshooting.md for cross-cutting installation, key/shape, backend, data, and optional-dependency failures. Check references/repo-provenance.md before deciding whether this skill matches a changed NeuroMANCER checkout.