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falcon
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Precision Inference for Complex Systems
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falcon
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# Falcon [](https://github.com/cweniger/falcon/actions/workflows/tests.yml) [](https://codecov.io/gh/cweniger/falcon) [](https://cweniger.github.io/falcon/) Falcon (Factorized Adaptive Learning of Conditional Orchestrated Networks) is a CLI-driven Python framework for **simulation-based inference (SBI)** with large, expensive simulators. Born in astrophysics, built for any domain with complex forward models — break your model into components and Falcon jointly infers their parameters. - **Composable** — define multi-component models as a graph of simulators in YAML, each wrapped with a thin Python interface, regardless of framework. - **Adaptive** — steers simulations toward high-posterior regions as training progresses, focusing compute where it matters. - **Concurrent** — trains neural posterior estimators across heterogeneous parameter blocks in parallel, using Ray for distributed execution. - **Batteries included** — ships with neural spline flows, data embeddings (including CNN/transformer support), and built-in experiment tracking via WandB. ## Installation ```bash pip install falcon-sbi ``` For development: ```bash git clone https://github.com/cweniger/falcon.git cd falcon pip install -e ".[all]" ``` ## Quick Start Run the minimal example (a 3-parameter Gaussian inference problem): ```bash cd examples/01_minimal falcon launch -o output/run_01 falcon sample posterior -o output/run_01 ``` This trains a neural posterior estimator on simulated data, then draws 1000 posterior samples. Results are saved under `output/run_01/`. ## How It Works You define a directed graph of random variables in `config.yml`. Each node has a **simulator** (forward model) and optionally an **estimator** (learned posterior). Falcon iterates between simulating data and training the estimator, automatically managing the sample buffer. ```yaml graph: z: # Latent parameters evidence: [x] simulator: _target_: falcon.priors.Product priors: - ['uniform', -5.0, 5.0] estimator: _target_: falcon.estimators.Flow x: # Observations parents: [z] simulator: _target_: model.Simulate observed: "./data/obs.npz['x']" ``` ## CLI ```bash falcon launch [-o DIR] [-c CONFIG] [key=value ...] falcon sample prior|posterior|proposal|ppd -o DIR falcon graph # Visualize graph structure ``` ## Examples | Example | Description | |---------|-------------| | [`01_minimal`](examples/01_minimal) | Basic 3-parameter inference | | [`02_bimodal`](examples/02_bimodal) | 10D bimodal posterior with training strategies | | [`03_composite`](examples/03_composite) | Multi-node graph with image embeddings | | [`04_gaussian`](examples/04_gaussian) | Gaussian inference | | [`05_linear_regression`](examples/05_linear_regression) | Linear regression | ## Documentation For tutorials, configuration reference, and API docs, see **[cweniger.github.io/falcon](https://cweniger.github.io/falcon/)**. ## Citation ```bibtex @software{falcon2024, title = {Falcon: Distributed Dynamic Simulation-Based Inference}, author = {Weniger, Christoph}, year = {2024}, url = {https://github.com/cweniger/falcon} } ``` ## License MIT License - see [LICENSE](LICENSE) for details.
