activity
20172026
most citedBayesian inference of an uncertain generalized diffusion operator

7 citations · 9 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Efficient Closure Learning in Dynamical Systems: A Comparison of Data-Assimilated and Embedded Approaches

Rileigh Bandy, Teresa Portone, Mohammad Khalil +2

Increased computing power has transformed computational science and improved predictive capabilities for complex systems, but many phenomena still exceed current simulation capabil…

stat.ME2026

Duality and Error for Predictively Oriented Inference

Aurya Javeed, Drew P. Kouri, Teresa Portone +1

Predictively oriented (PrO) inference quantifies uncertainty by selecting a distribution over model parameters to optimize a scoring rule applied to the induced predictive distribu…

stat.ML2025

Scalable extensions to given-data Sobol' index estimators

Teresa Portone, Bert Debusschere, Samantha Yang +2

Given-data methods for variance-based sensitivity analysis have significantly advanced the feasibility of Sobol' index computation for computationally expensive models and models w…

stat.AP2021★ 7 cited

Bayesian inference of an uncertain generalized diffusion operator

Teresa Portone, Robert D. Moser

This paper defines a novel Bayesian inverse problem to infer an infinite-dimensional uncertain operator appearing in a differential equation, whose action on an observable state va…

cs.CE2017★ 2 cited

A Stochastic Operator Approach to Model Inadequacy with Applications to Contaminant Transport

Teresa Portone, Damon McDougall, Robert D. Moser

The mathematical models used to represent physical phenomena are generally known to be imperfect representations of reality. Model inadequacies arise for numerous reasons, such as…