Dissipative Particle Dynamics
Bayesian Uncertainty Quantification As the name would suggest, uncertainty quantification (UQ) is a broad field which involves the mathematical treatment of uncertainty in physical and model systems. Such uncertainties are ubiquitous - whether studying inherently stochastic phenomena such as branching processes or deterministic systems corrupted by observational, systemic, or sampling noise, uncertainty quantification almost surely has applications to the questions of interest. A common subset of UQ is parameter estimation, which concerns the inverse problem, i.e., trying to map the outputs of a system back to the inputs which generated them. In the presence of uncertainty, many sets of parameters could feasibly yield identical data; the Bayesian approach to this problem (Bayesian uncertainty quantification) places a rigorous probabilistic framework on this problem in order to derive the target distributions. I work with collaborators from the CSElab at ETH Zürich on an approach to Bayesian UQ which uses Markov chain Monte Carlo methods; this high-performance framework, known as Pi4U, can be adapted to a variety of expensive mathematical models (including particle simulations) to produce some very nice visualizations of high-dimensional samples from the posterior, such as those seen below for a network epidemic model (collaboration with Zhizhong Chen, Karen Larson, Panagiotis Hadjidoukas, Costas Papadimitriou, Petros Koumoutsakos, and Anastasios Matzavinos). |