Contributed Papers

Advances in Statistical Modeling and Inference for Complex Physical Systems

Tue, Aug 5, 8:30 AM - 10:20 AM Room CC-210 Music City Center

Section on Physical and Engineering Sciences

About this session

This session brings together research on statistical modeling tools for complex systems and new insights on inference and uncertainty quantification for problems in the physical and engineering sciences.

7 Presentations

Efficient Gaussian Process Modeling for Replicated and Censored Data in Manufacturing Applications

8:35 AM - 8:50 AM

Co-authors: Ying Zhang, Ying Zhang, Emily Kang (University of Cincinnati), Shuo Li (Procter & Gamble)

Censored data Gaussian process Replication Truncated multivariate normal

Two-Step Bayesian Estimation of Sparse Dynamical Systems Using Data-Driven Closure Models

8:50 AM - 9:05 AM

Co-authors: Daniel Drennan (Department of Statistics, Texas A&M University), Toryn Schafer (Texas A&M University), Daniel Drennan (Department of Statistics, Texas A&M University), Kyle Neal (Sandia National Laboratories), Moe Khalil (Sandia National Laboratories), Teresa Portone (Sandia National Laboratories), Rileigh Bandy (Sandia National Laboratories)

Spline estimation Gaussian processes Koopman operators dynamical systems missing data uncertainty quantification

Ionospheric Observations from the ISS: Overcoming Noise Challenges in Signal Extraction

9:05 AM - 9:20 AM

Co-authors: Rachel Ulrich, Rachel Ulrich, Kelly Moran (Los Alamos National Laboratory), Carlos Maldonado (Los Alamos National Laboratory)

signal processing Gaussian Processes background estimation time series astrophysics Ionospheric science

Monotonic warpings for additive and deep Gaussian processes

9:20 AM - 9:35 AM

Co-authors: Steven Barnett (Virginia Tech), Steven Barnett (Virginia Tech)

computer experiment surrogate modeling constrained response surface elliptical slice sampling uncertainty quantification Bayesian inference

WITHDRAWN: A scalable Bayesian approach to spectral line detection and galaxy redshift estimation

9:35 AM - 9:50 AM

Co-authors: Alexander Kuhn (University of Minnesota), Bonnabelle Zabelle (University of Minnesota), Alexander Kuhn (University of Minnesota), Sara Algeri (University of Minnesota), Galin Jones (University of Minnesota), Claudia Scarlata (University of Minnesota)

Bayes factors Astrostatistics Laplace approximation

Neural Posterior Estimation for Inferring Weak Lensing Shear and Convergence from Pixels

9:50 AM - 10:05 AM

Co-authors: Timothy White (University of Michigan), Timothy White (University of Michigan), Shreyas Chandrashekaran (University of Michigan), Camille Avestruz (University of Michigan), Jeffrey Regier (University of Michigan)

neural posterior estimation weak gravitational lensing likelihood-free inference variational inference cosmology astronomical images

Statistical Emulators for Inferring Planet Formation Conditions

10:05 AM - 10:20 AM

Co-authors: Anirban Mondal (Case Western Reserve University), Anirban Mondal (Case Western Reserve University)

Emulators Gaussian Process Stochastic Simulator Astrostatistics Planet formation