activity
20152025
most citedPhysics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

stat.ME2025

Vecchia approximated Bayesian heteroskedastic Gaussian processes

Parul V. Patil, Robert B. Gramacy, Cayelan C. Carey +1

Many computer simulations are stochastic and exhibit input dependent noise. In such situations, heteroskedastic Gaussian processes (hetGPs) make ideal surrogates as they estimate a…

cs.LG2025

Investigating a Model-Agnostic and Imputation-Free Approach for Irregularly-Sampled Multivariate Time-Series Modeling

Abhilash Neog, Arka Daw, Sepideh Fatemi Khorasgani +10

Modeling Irregularly-sampled and Multivariate Time Series (IMTS) is crucial across a variety of applications where different sets of variates may be missing at different time-steps…

stat.AP2024

Synthesizing data products, mathematical models, and observational measurements for lake temperature forecasting

Maike F. Holthuijzen, Robert B. Gramacy, Cayelan C. Carey +2

We present a novel forecasting framework for lake water temperature, which is crucial for managing lake ecosystems and drinking water resources. The General Lake Model (GLM) has be…

cs.LG2019★ 1 cited

Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling

Arka Daw, R. Quinn Thomas, Cayelan C. Carey +3

To simultaneously address the rising need of expressing uncertainties in deep learning models along with producing model outputs which are consistent with the known scientific know…

cs.DC2015

GRAPLEr: A Distributed Collaborative Environment for Lake Ecosystem Modeling that Integrates Overlay Networks, High-throughput Computing, and Web Services

Kensworth Subratie, Saumitra Aditya, Renato Figueiredo +2

The GLEON Research And PRAGMA Lake Expedition -- GRAPLE -- is a collaborative effort between computer science and lake ecology researchers. It aims to improve our understanding and…