kipe

Kalman-based identifiability and parameter estimation

kipe estimates the parameters of dynamical models, e.g., PDE models, from time series of measurements, with the reduced-order unscented Kalman filter (ROUKF). The filter needs only forward runs of your model, no derivatives, through a lean interface. Around the estimation, kipe generates synthetic measurement data for twin experiments, from a run of the model or from the checkpoints of an earlier run, and will analyze the identifiability of the parameters with the Fisher information (planned).

Key features

• Solver-agnostic: Connect your model through a small forward-solver interface
• Derivative-free: Optimizer is based on the reduced-order unscented Kalman filter (ROUKF) and hence it needs only forward runs.
• Modular observation operators: New observation operators can be implemented easily by extending the library.
• Twin experiments: Generate synthetic measurements from a model run.
• Reproducible runs: Keep results, logs, and run provenance together.

Status: early development. The core works (ROUKF estimation, synthetic data, plotting, run provenance), as does a first FEniCSx backend for measurements on finite element meshes, but the study file format and the Python API may still change. Feedback is very welcome: please open an issue.

Installation

kipe needs Python ≥ 3.12 and an MPI library (for mpi4py):

pip install kipe
pip install mpich  # only if there is no MPI library on your system

For FEniCSx models, install kipe into your FEniCSx (dolfinx 0.9) environment.

Quick start

A twin experiment with the FitzHugh–Nagumo model: synthetic data from the true parameters, then the estimation from a wrong initial guess.

git clone https://github.com/cardiomath/kipe
cd kipe/examples/fitzhugh_nagumo
kipe synthesis study.yaml
kipe estimation study.yaml
kipe plot study.yaml --truth  # --terminal without a display

Documentation

cardiomath.github.io/kipe: getting started, running a study, measurements, the study file reference, the theory, and how to extend kipe.

Development

git clone https://github.com/cardiomath/kipe
cd kipe
pip install -e ".[dev]"
pre-commit install --hook-type pre-commit --hook-type commit-msg
pytest

See Development in the documentation.

License

MIT, see LICENSE. If you use kipe, please cite it as described in CITATION.cff.