7 citations · 15 across the 11 of their papers we have counts for
12 papers
A Statistician's Overview of Physics-Informed Neural Networks for Spatio-Temporal Data
Christopher K. Wikle, Joshua North, Giri Gopalan +1
The recent success of deep neural network models with physical constraints (so-called, Physics-Informed Neural Networks, PINNs) has led to renewed interest in the incorporation of…
Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks
Rachel Longjohn, Giri Gopalan, Emily Casleton
Modern artificial intelligence is supported by machine learning models (e.g., foundation models) that are pretrained on a massive data corpus and then adapted to solve a variety of…
Length scale estimation of excited quantum oscillators
Tyler Volkoff, Giri Gopalan
Massive quantum oscillators are finding increasing applications in proposals for high-precision quantum sensors and interferometric detection of weak forces. Although optimal estim…
Emulation with uncertainty quantification of regional sea-level change caused by the Antarctic Ice Sheet
Myungsoo Yoo, Giri Gopalan, Matthew J. Hoffman +4
Projecting sea-level change in various climate-change scenarios typically involves running forward simulations of the Earth's gravitational, rotational and deformational (GRD) resp…
A Review of Bayesian Modelling in Glaciology
Giri Gopalan, Andrew Zammit-Mangion, Felicity McCormack
Bayesian methods for modelling and inference are being increasingly used in the cryospheric sciences, and glaciology in particular. Here, we present a review of recent works in gla…
A higher-order singular value decomposition tensor emulator for spatio-temporal simulators
Giri Gopalan, Christopher K. Wikle
We introduce methodology to construct an emulator for environmental and ecological spatio-temporal processes that uses the higher order singular value decomposition (HOSVD) as an e…
A Hierarchical Spatio-Temporal Statistical Model Motivated by Glaciology
Giri Gopalan, Birgir Hrafnkelsson, Christopher K. Wikle +4
In this paper, we extend and analyze a Bayesian hierarchical spatio-temporal model for physical systems. A novelty is to model the discrepancy between the output of a computer simu…
Application of the Trend Filtering Algorithm for Photometric Time Series Data
Giri Gopalan, Peter Plavchan, Julian van Eyken +3
Detecting transient light curves (e.g., transiting planets) requires high precision data, and thus it is important to effectively filter systematic trends affecting ground based wi…
Quantification of observed prior and likelihood information in parametric Bayesian modeling
Giri Gopalan
Two data-dependent information metrics are developed to quantify the information of the prior and likelihood functions within a parametric Bayesian model, one of which is closely r…
FastGP: An R Package for Gaussian Processes
Giri Gopalan, Luke Bornn
Despite their promise and ubiquity, Gaussian processes (GPs) can be difficult to use in practice due to the computational impediments of fitting and sampling from them. Here we dis…
Admissibility of a posterior predictive decision rule
Giri Gopalan
Recent decades have seen an interest in prediction problems for which Bayesian methodology has been used ubiquitously. Sampling from or approximating the posterior predictive distr…
Classifying X-ray Binaries: A Probabilistic Approach
Giri Gopalan, Saeqa Dil Vrtilek, Luke Bornn
In X-ray binary star systems consisting of a compact object that accretes material from an orbiting secondary star, there is no straightforward means to decide if the compact objec…