anypinn 0.5.2

...

AnyPINN

Work in Progress — This project is under active development and APIs may change. If you run into any issues, please open an issue on GitHub.

A modular Python library for solving differential equations with Physics-Informed Neural Networks.

AnyPINN lets you go from zero to a running PINN experiment in seconds, or give you the full control to define custom physics, constraints, and training loops. You decide how deep to go.

Quick Start

The fastest way to start is the bootstrap CLI. It scaffolds a complete, runnable project interactively:

uvx anypinn create my-project

or

pip install anypinn
anypinn create my-project

? Choose a starting point:
  > SIR Epidemic Model
    ...
    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 metadata & dependencies
  ✓  ode.py           your ODE definition
  ✓  config.py        hyperparameters with sensible defaults
  ✓  train.py         ready-to-run training script
  ✓  data/            data directory

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

All prompts are also available as flags to skip the interactive flow:

anypinn create my-project \
  --template sir \
  --data synthetic \
  --lightning

Flag	Values	Description
--template, -t	built-in template name, custom, or blank	Starting template
--list-templates, -l	—	Print all templates with descriptions and exit
--data, -d	synthetic, csv	Training data source
--lightning / --no-lightning	—	Include PyTorch Lightning wrapper

Who Is This For?

AnyPINN is built around progressive complexity — start simple, go deeper only when you need to.

User	Goal	How
Experimenter	Run a known problem, tweak parameters, see results	Pick a built-in template, change config, press start
Researcher	Define new physics or custom constraints	Subclass Constraint and Problem, use the provided training engine
Framework builder	Custom training loops, novel architectures	Use anypinn.core directly — zero Lightning required

Installation

Prerequisites: Python 3.11+, uv (recommended).

# Install from source (development)
git clone https://github.com/your-org/anypinn
cd anypinn
uv sync

Examples

Ready-made examples live in examples/. Each is a self-contained script covering a different ODE system (epidemic models, oscillators, predator-prey dynamics, and more). Browse the directory and run any of them directly:

uv run python examples/<name>/<name>.py

Example	Description
examples/exponential_decay/	Start here. Minimal core-only script (~80 lines). Learns decay rate k with a plain PyTorch loop — no Lightning.
examples/sir_inverse/	Full SIR epidemic model (Lightning stack)
examples/seir_inverse/	SEIR epidemic model (Lightning stack)
examples/damped_oscillator/	Damped harmonic oscillator (Lightning stack)
examples/lotka_volterra/	Predator-prey dynamics (Lightning stack)

Defining Your Own Problem

If you want to go beyond the built-in templates, here is the full workflow for defining a custom ODE inverse problem.

1: Define the ODE

Implement a function matching the ODECallable protocol:

from torch import Tensor
from anypinn.core import ArgsRegistry

def my_ode(x: Tensor, y: Tensor, args: ArgsRegistry) -> Tensor:
    """Return dy/dx given current state y and position x."""
    k = args["k"](x)        # learnable or fixed parameter
    return -k * y           # simple exponential decay

2: Configure hyperparameters

from dataclasses import dataclass
from anypinn.problems import ODEHyperparameters

@dataclass(frozen=True, kw_only=True)
class MyHyperparameters(ODEHyperparameters):
    pde_weight: float = 1.0
    ic_weight: float = 10.0
    data_weight: float = 5.0

3: Build the problem

from anypinn.problems import ODEInverseProblem, ODEProperties

props = ODEProperties(ode=my_ode, args={"k": param}, y0=y0)
problem = ODEInverseProblem(
    ode_props=props,
    fields={"u": field},
    params={"k": param},
    hp=hp,
)

4: Train

import pytorch_lightning as pl
from anypinn.lightning import PINNModule

# With Lightning (batteries included)
module = PINNModule(problem, hp)
trainer = pl.Trainer(max_epochs=50_000)
trainer.fit(module, datamodule=dm)

# Or with your own training loop (core only, no Lightning)
optimizer = torch.optim.Adam(problem.parameters(), lr=1e-3)
for batch in dataloader:
    optimizer.zero_grad()
    loss = problem.training_loss(batch, log=my_log_fn)
    loss.backward()
    optimizer.step()

Architecture

AnyPINN is split into four layers with a strict dependency direction — outer layers depend on inner ones, never the reverse.

graph TD
    EXP["Your Experiment / Generated Project"]

    EXP --> CAT
    EXP --> LIT

    subgraph CAT["anypinn.catalog"]
        direction LR
        CA1[SIR / SEIR]
        CA2[DampedOscillator]
        CA3[LotkaVolterra]
    end

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

    subgraph PROB["anypinn.problems"]
        direction LR
        P1[ResidualsConstraint]
        P2[ICConstraint]
        P3[DataConstraint]
        P4[ODEInverseProblem]
    end

    subgraph CORE["anypinn.core (standalone · pure PyTorch)"]
        direction LR
        C1[Problem · Constraint]
        C2[Field · Parameter]
        C3[Config · Context]
    end

    CAT -->|depends on| PROB
    CAT -->|depends on| CORE
    LIT -->|depends on| CORE
    PROB -->|depends on| CORE

anypinn.core — The Math Layer

Pure PyTorch. Defines what a PINN problem is, with no opinions about training.

• Problem — Aggregates constraints, fields, and parameters. Provides training_loss() and predict().
• Constraint (ABC) — A single loss term. Subclass it to express any physics equation, boundary condition, or data-matching objective.
• Field — MLP mapping input coordinates to state variables (e.g., t → [S, I, R]).
• Parameter — Learnable scalar or function-valued parameter (e.g., β in SIR).
• InferredContext — Runtime domain bounds and validation references, extracted from data and injected into constraints automatically.

anypinn.lightning — The Training Engine (optional)

A thin wrapper plugging a Problem into PyTorch Lightning:

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

anypinn.problems — ODE Building Blocks

Ready-made constraints for ODE inverse problems:

• ResidualsConstraint — ‖dy/dt − f(t, y)‖² via autograd
• ICConstraint — ‖y(t₀) − y₀‖²
• DataConstraint — ‖prediction − observed data‖²
• ODEInverseProblem — Composes all three with configurable weights

anypinn.catalog — Problem-Specific Building Blocks

Drop-in ODE functions and DataModules for specific systems. See anypinn/catalog/ for the full list.

Tooling

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

All common tasks (test, lint, format, type-check, docs) are available via just.

Contributing

See CONTRIBUTING.md for setup instructions, code style guidelines, and the pull request workflow.