anypinn 0.2.1

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AnyPINN

A modular, extensible Python library for solving differential equations using Physics-Informed Neural Networks (PINNs). Built for scalability — from one-click experiments to fully custom problem definitions.

Philosophy

AnyPINN is designed around two principles:

1. Separation of concerns. The mathematical problem definition is completely decoupled from the training engine. You can use one without the other.
2. Progressive complexity. Start simple, go deep only when you need to.

This means the library serves three types of users:

User	Goal	How
Experimenter	Run a known problem, tweak parameters, see results	Pick a built-in problem, change config, press start
Researcher	Define a new problem with custom physics	Implement Constraint and Problem, use the provided training engine
Framework builder	Custom training loops, novel architectures	Use the core abstractions directly, skip Lightning entirely

Architecture

The library is split into two independent layers:

graph TD
    EXP["Your Experiment<br/><i>examples/ or your own script</i>"]

    EXP --> LIT
    EXP --> CORE

    subgraph LIT["anypinn.lightning (optional)"]
        direction LR
        L1[PINNModule]
        L2[Callbacks]
        L3[PINNDataModule]
        L4[PredictionsWriter]
    end

    subgraph CORE["anypinn.core (standalone)"]
        direction LR
        C1["Problem — Constraint (ABC)"]
        C2["Field — MLP networks"]
        C3["Parameter — learnable scalars"]
        C4["Config — dataclass configs"]
        C5["Context — runtime domain info"]
        C6["Validation — ground truth refs"]
    end

    LIT -->|depends on| CORE

Core (anypinn.core) — The Math Layer

The core is a pure PyTorch library. It defines what a PINN problem is, with no opinions about how you train it.

• Problem — Aggregates constraints, fields, and parameters. Provides training_loss() and predict().
• Constraint (abstract) — A single loss term. Subclass it to define any physics equation, boundary condition, or data-matching loss.
• Field — An MLP that maps input coordinates to state variables (e.g., t -> [S, I, R]).
• Parameter — A learnable scalar or function-valued parameter (e.g., beta in an SIR model).
• InferredContext — Runtime information (domain bounds, validation data) extracted from training data and injected into constraints.

You can use Problem.training_loss() inside any training loop — plain PyTorch, Hugging Face Accelerate, or anything else.

Lightning (anypinn.lightning) — The Training Engine (Optional)

A thin wrapper that plugs a Problem into PyTorch Lightning. Use it when you want batteries-included training with minimal boilerplate:

• PINNModule — Wraps a Problem as a LightningModule. Handles optimizer setup, context injection, and prediction.
• PINNDataModule — Abstract data module that manages data loading, collocation point generation, and context creation.
• Callbacks — SMMA-based early stopping, formatted progress bars, prediction writers, data scaling.

Problems (anypinn.problems) — Ready-Made Templates

Pre-built constraint sets for common problem types:

• ODE layer (ode.py): ResidualsConstraint, ICConstraint, DataConstraint — covers most ODE inverse problems out of the box.
• SIR Inverse (sir_inverse.py): Full and reduced SIR model implementations.

Data Flow

Training

graph TD
    DS["Data Source<br/><i>CSV or synthetic</i>"]
    DM["PINNDataModule"]
    DM1["load_data() / gen_data() — produce (x, y) pairs"]
    DM2["gen_coll() — produce collocation points"]
    DM3["DataCallback.transform_data() — optional scaling"]
    DM4["setup()"]
    CTX["InferredContext<br/><i>domain bounds, resolved validation</i>"]
    DSET["PINNDataset<br/><i>batches of labeled data + collocation points</i>"]
    STEP["PINNModule.training_step(batch)"]
    LOSS["Problem.training_loss(batch)"]
    C1["Constraint₁.loss() — ODE residuals"]
    C2["Constraint₂.loss() — initial conditions"]
    C3["Constraint₃.loss() — data matching"]
    BP["Σ weighted losses → backprop → Adam + optional scheduler"]

    DS --> DM
    DM --- DM1
    DM --- DM2
    DM --- DM3
    DM --- DM4
    DM --> CTX --> DSET --> STEP --> LOSS
    LOSS --> C1
    LOSS --> C2
    LOSS --> C3
    C1 --> BP
    C2 --> BP
    C3 --> BP

Prediction

graph TD
    PS["PINNModule.predict_step(batch)"]
    PP["Problem.predict(batch)"]
    F["Field(x) → state variables (unscaled)"]
    P["Parameter(x) → learned parameters"]
    T["true_values(x) → ground truth (if available)"]
    OUT["((x, y_pred), params_dict, true_values_dict)"]

    PS --> PP
    PP --> F
    PP --> P
    PP --> T
    F --> OUT
    P --> OUT
    T --> OUT

Getting Started

Installation

uv sync

Run an Example

cd examples/sir_inverse
python sir_inverse.py

Implement a New Problem

1. Define your ODE as a callable matching the ODECallable protocol:

def my_ode(x: Tensor, y: Tensor, args: ArgsRegistry) -> Tensor:
    # Return dy/dx
    ...

2. Configure hyperparameters:

@dataclass(frozen=True, kw_only=True)
class MyHyperparameters(PINNHyperparameters):
    pde_weight: float = 1.0
    data_weight: float = 1.0

3. Build the problem from constraints:

problem = MyProblem(
    constraints=[
        ResidualsConstraint(field, ode_props, weight=hp.pde_weight),
        DataConstraint(predict_fn, weight=hp.data_weight),
    ],
    fields={"u": field},
    params={"k": param},
)

4. Train with Lightning or your own loop:

# With Lightning
module = PINNModule(problem, hp)
trainer = pl.Trainer(max_epochs=50000)
trainer.fit(module, datamodule=dm)

# Or plain PyTorch
for batch in dataloader:
    loss = problem.training_loss(batch, log=my_log_fn)
    loss.backward()
    optimizer.step()

See examples/ for complete implementations:

• sir_inverse/ — SIR epidemic model (full, reduced, hospitalized variants)
• damped_oscillator/ — Damped harmonic oscillator
• lotka_volterra/ — Predator-prey dynamics
• seir_inverse/ — SEIR epidemic model

Future: Bootstrap CLI (anypinn create)

Planned: a scaffolding tool inspired by npx create-next-app that lets you bootstrap a new PINN project interactively:

$ anypinn create my-project

? Choose a starting point:
  > From a template (SIR, SEIR, Lotka-Volterra, Damped Oscillator, ...)
    Define a new ODE problem
    Blank project

? Select training data source:
  > Generate synthetic data
    Load from CSV

? Include Lightning training wrapper? (Y/n)

Creating my-project/...
  my_problem.py     — problem definition
  train.py          — training script
  config.py         — hyperparameters
  data/             — data directory
Done.

This will lower the barrier for experimenters who want to try a known problem with their own data without writing boilerplate.

Development

Tooling

Tool	Purpose
uv	Dependency management
Nox	Task automation
Ruff	Linting and formatting
pytest	Testing
mypy	Strict type checking

Commands

uv run nox -s test           # Run tests (100% coverage required)
uv run nox -s lint           # Check code style
uv run nox -s fmt            # Format code (isort + ruff)
uv run nox -s lint_fix       # Auto-fix linting issues
uv run nox -s type_check     # MyPy strict type checking
uv run nox -s docs           # Build documentation
uv run nox -s docs_serve     # Serve docs locally

Contributing

When contributing:

• Follow the existing code style (Ruff, line length 99, absolute imports only)
• Keep the two-layer separation: core stays pure PyTorch, Lightning stays optional
• If you change the architecture or data flow, update both CLAUDE.md and this README to reflect the changes