## SI2-SSI: Integrating Data with Complex Predictive Models under Uncertainty: An Extensible Software Framework for Large-Scale Bayesian Inversion

presentation

posted on 05.02.2020 by Umberto Villa, Youssef Marzouk, Noemi Petra, Omar Ghattas#### presentation

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Recent years have seen a massive
explosion of datasets across all areas of science, engineering,
technology, medicine, and the social sciences. The central questions
are: How do we optimally learn from data through the lens of models? And
how do we do so taking into account uncertainty in both data and
models?

These questions can be mathematically
framed as Bayesian inverse problems. While powerful and sophisticated
approaches have been developed to tackle these problems, such methods
are often challenging to implement and typically require first and
second order derivatives that are not always available in existing
computational models.

We present an extensible
software framework MUQ-hIPPYlib that overcomes this hurdle by providing
unprecedented access to state-of-the-art algorithms for deterministic
and Bayesian inverse problems. MUQ provides a spectrum of powerful
Bayesian inversion models and algorithms, but expects forward models to
come equipped with gradients/Hessians to permit large-scale solution.
hIPPYlib implements powerful large-scale gradient/Hessian-based solvers
in an environment that can automatically generate needed derivatives,
but it lacks full Bayesian capabilities. By integrating these two
libraries, we created a robust, scalable, and efficient software
framework that realizes the benefits of each to tackle complex
large-scale Bayesian inverse problems across a broad spectrum of
scientific and engineering areas.

Our ultimate
goal is to make these advanced inversion capabilities available to a
broader scientific community, to provide an environment that accelerates
scientific discovery.

### Categories

### Funding

### Collaborative Research: SI2-SSI: Integrating Data with Complex Predictive Models under Uncertainty: An Extensible Software Framework for Large-Scale Bayesian Inversion

Directorate for Computer & Information Science & Engineering

Find out more...### Collaborative Research: SI2-SSI: Integrating Data with Complex Predictive Models under Uncertainty: An Extensible Software Framework for Large-Scale Bayesian Inversion

Directorate for Computer & Information Science & Engineering

Find out more...### Collaborative Research: SI2-SSI: Integrating Data with Complex Predictive Models under Uncertainty: An Extensible Software Framework for Large-Scale Bayesian Inversion

Directorate for Computer & Information Science & Engineering

Find out more...