This folder implements GP-Bayesian parameter estimation for systems of ODEs that are linear in the parameters of interest.
main.py: run an experiment and show/save results.step1_generate_data.py: generate experimental snapshot data.step3_estimate.py: solve the Bayesian parameter inference problem, selecting the regularization hyperparameters automatically. Construct the posterior.step4_plot.py: plotting tools.
step2_fitgps.py: fit Gaussian processes to data (same as the single-trajectory case).baseplots.py: utilities for plotting experiment results.bayes.py: Bayesian posterior models.gpkernels.py: GP kernels and regression matrices.pde_models.py: full-order partial differential equations models.wlstsq.py: weighted least-squares solvers.
config.py: configure settings (matplotlib, file/folder names, etc.).config_heat.py: simulation scenario for the heat equation problem.utils.py: define tools for loading data and saving figures.
runlog.sh: Log of commands used to run the experiments reported in the paper.
These folders are not tracked by git, but may be generated by running the code.
- data/: experiment results for visualizations.
- figures/: saved figures, organized by date.