bayesian-inverse-problems

Imaging Inverse Problems and Bayesian Computation - Python tutorials to learn about (accelerated) sampling for uncertainty quantification and other advanced inferences

Implementation of hierarchical Bayesian longitudinal models to estimate differential equation parameters.

Source code for Bayesian urban inversion OSSE [Kunik et al., 2019] written in R and equipped with sample inputs (run it out-of-the-box!)

This contains the codes used in the paper 'A randomized Multi-index sequential Monte Carlo method'.

A small collection of python-scripts associated with Gaussian process emulators in Bayesian inverse problems

Official code and reproducibility suite for 'Parameter-Space Error Localization and Posterior Fidelity of Physics-Informed Neural Network Forward Surrogates in Bayesian Inverse Problems' — multi-PDE benchmarks, optimal transport bounds, and MCMC calibration.

Stochastic modelling of urban travel demand

Hilbert's sixth problem from Kolmogorov's axioms and the Boltzmann equation to the Navier Stokes equations, and the inverse passage: Bayesian estimation on the solution manifold of a differential equation, where the adjoint evolution of the data is a sufficient statistic for linear equations.