AnyPINN

Solve differential equations with Physics-Informed Neural Networks.
_{Modular. Training-agnostic. Inverse-problem-first.}

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.

AnyPINN lets you go from zero to a running PINN experiment in seconds, or gives you 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. Run it with uvx (ships with uv):

uvx anypinn create my-project

or with pipx:

pipx run 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
--help, -h	—	Show help and exit
--list-templates, -l	—	Print all templates with descriptions and exit
--template, -t	built-in template name, custom, or blank	Starting template
--data, -d	synthetic, csv	Training data source
--lightning, -L	—	Include PyTorch Lightning wrapper
--no-lightning, -NL	—	Exclude 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

💡 Examples

The examples/ directory has ready-made, self-contained scripts covering epidemic models, oscillators, predator-prey dynamics, and more — from a minimal ~80-line core-only script to full Lightning stacks. They're a great source of inspiration when defining your own problem.

🔬 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

Loading

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, config-driven collocation sampling, and context creation. Collocation strategy is selected via TrainingDataConfig.collocation_sampler ("random", "uniform", "latin_hypercube", "log_uniform_1d", or "adaptive").
• 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
mise (optional)	Toolchain provisioning
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.