MUQ-hIPPylib Lightning Slide NSF SI2 PI meeting

Slide to accompany 1 minute lightning talk at the NSF SI2 PI meeting. Our project is "Integrating Data with Complex Predictive Models under Uncertainty: An Extensible Software Framework for Large-Scale Bayesian Inversion"<div><br></div><div>The goal of our project is to enable the widespread use of modern Bayesian inference algorithms by developing a software framework and a sustainable community of users and developers. Our framework is built on top of two major software packages: the MIT Uncertainty Quantification Library (MUQ), which provides tools for advanced statistical modeling and sampling approaches, as well as hIPPylib, which provides state-of-the-art tools for large-scale deterministic inverse problems built from partial differential equations.