Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via Operator Learning with Limited Data

no code implementations • 20 Mar 2023 • Joseph Hart, Mamikon Gulian, Indu Manickam, Laura Swiler

In complex large-scale systems such as climate, important effects are caused by a combination of confounding processes that are not fully observable.

Operator learning Uncertainty Quantification

Error-in-variables modelling for operator learning

no code implementations • 22 Apr 2022 • Ravi G. Patel, Indu Manickam, Myoungkyu Lee, Mamikon Gulian

We propose error-in-variables (EiV) models for two operator regression methods, MOR-Physics and DeepONet, and demonstrate that these new models reduce bias in the presence of noisy independent variables for a variety of operator learning problems.

Model Discovery Operator learning +1

IdeoTrace: A Framework for Ideology Tracing with a Case Study on the 2016 U.S. Presidential Election

no code implementations • 21 May 2019 • Indu Manickam, Andrew S. Lan, Gautam Dasarathy, Richard G. Baraniuk

We apply this framework to the last two months of the election period for a group of 47508 Twitter users and demonstrate that both liberal and conservative users became more polarized over time.