anypinn 0.4.1

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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

Bootstrap CLI (anypinn create)

A scaffolding tool inspired by create-next-app that bootstraps a new PINN project interactively.

anypinn create my-project

The CLI walks you through three choices and generates a complete, runnable project:

$ anypinn create my-project

? Choose a starting point:
  > SIR Epidemic Model
    SEIR Epidemic Model
    Damped Oscillator
    Lotka-Volterra
    Custom ODE
    Blank project

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

? Include Lightning training wrapper? (Y/n)

Creating my-project/...
  pyproject.toml   — project dependencies
  ode.py           — mathematical definition
  config.py        — training configuration
  train.py         — execution script
  data/            — data directory

Done! cd my-project && uv sync && uv run train.py

Options

All prompts can also be passed as flags to skip the interactive flow:

Flag	Values	Description
--template, -t	sir, seir, damped-oscillator, lotka-volterra, custom, blank	Starting template
--data, -d	synthetic, csv	Training data source
--lightning / --no-lightning	—	Include PyTorch Lightning wrapper

Generated Project

The scaffolded project reflects your choices:

• pyproject.toml — Dependencies, conditionally including lightning and torchdiffeq
• ode.py — ODE function matching the ODECallable protocol
• config.py — Hyperparameters with sensible defaults for your data source
• train.py — Full training script (Lightning Trainer or a plain PyTorch loop, depending on your choice)
• data/ — Data directory (pre-populated with CSV placeholders when relevant)

Development

Tooling

Tool	Purpose
uv	Dependency management
just	Task automation
Ruff	Linting and formatting
pytest	Testing
ty	Type checking

Commands

just test           # Run tests with coverage
just lint           # Check code style
just fmt            # Format code (isort + ruff)
just lint-fix       # Auto-fix linting issues
just check          # ty type checking
just docs           # Build documentation
just docs-serve     # Serve docs locally
just ci             # Run lint + check + test

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