Local area network analysis using end-to-end delay tomography. - Earl Lawrence; George Michailidis; Vijay N. Nair (SIGMETRICS Perform. Evaluation Rev., 2005)

Analysis of Multi-domain Complex Simulation Studies. - James R. Gattiker; Earl Lawrence; David Higdon (ICCSA (3), 2006)

Bayesian Inference for Multivariate Ordinal Data Using Parameter Expansion. - Earl Lawrence; Derek Bingham; Chuanhai Liu; Vijayan N. Nair (Technometrics, 2008)

Computer Model Calibration Using the Ensemble Kalman Filter. - David Higdon; James R. Gattiker; Earl Lawrence; Charles S. Jackson; Michael Tobis; Matthew T. Pratola; Salman Habib 002; Katrin Heitmann; Steve Price (Technometrics, 2013)

Model Bank State Estimation for Power Grids Using Importance Sampling. - Earl Lawrence; Scott A. Vander Wiel; Russell Bent (Technometrics, 2013)

Special Issue: Conference on Data Analysis (CoDA) - Exploring Data-Focused Research across the Department of Energy. - Kary L. Myers; Earl Lawrence; Hugh A. Chipman (Technometrics, 2013)

Specification of the Ionosphere-Thermosphere Using the Ensemble Kalman Filter. - Humberto C. Godinez; Earl Lawrence; David Higdon; Aaron J. Ridley; Josef Koller; Alexei V. Klimenko (DyDESS, 2014)

Partitioning a Large Simulation as It Runs. - Kary L. Myers; Earl Lawrence; Michael Fugate; Claire McKay Bowen; Lawrence Ticknor; Jon Woodring; Joanne Wendelberger; Jim Ahrens (Technometrics, 2016)

Visualization of Uncertainty for Computationally Intensive Simulations Using High Fidelity Emulators. - Ayan Biswas; Kelly R. Moran; Earl Lawrence; James P. Ahrens (SciVis, 2018)

Emulating Satellite Drag from Large Simulation Experiments. - Furong Sun; Robert B. Gramacy; Benjamin Haaland; Earl Lawrence; Andrew Walker (SIAM/ASA J. Uncertain. Quantification, 2019)

Relationship-aware Multivariate Sampling Strategy for Scientific Simulation Data. - Subhashis Hazarika; Ayan Biswas; Phillip J. Wolfram; Earl Lawrence; Nathan M. Urban (IEEE VIS (Short Papers), 2020)

An Interactive Exploration Tool for High-Dimensional Datasets: A Shock Physics Case Study. - Ayan Biswas; Christopher M. Biwer; David J. Walters; James P. Ahrens; Devin Francom; Earl Lawrence; Richard L. Sandberg; D. Anthony Fredenburg; Cynthia Bolme (Comput. Sci. Eng., 2020)

Topological Segmentation and Tracking for Space Weather Modeling. - Divya Banesh; Natalie Klein; Steven Morley; Ayan Biswas; Vania Jordanova; Michael Henderson; Earl Lawrence (IEEE BigData, 2021)

Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks. - Divya Banesh; Nishant Panda; Ayan Biswas; Luke Van Roekel; Diane Oyen; Nathan M. Urban; Michael Grosskopf; Jonathan David Wolfe; Earl Lawrence (IEEE BigData, 2021)

Probabilistic Principal Component Analysis Guided Spatial Partitioning of Multivariate Ocean Biogeochemistry Data. - Subhashis Hazarika; Ayan Biswas; Earl Lawrence; Philip J. Wolfram (EnvirVis@EuroVis, 2021)

In Situ Climate Modeling for Analyzing Extreme Weather Events. - Soumya Dutta; Natalie Klein; Li Tang 007; Jonathan David Wolfe; Luke Van Roekel; James Joseph Benedict; Ayan Biswas; Earl Lawrence; Nathan M. Urban (ISAV@SC, 2021)

In-Situ Spatial Inference on Climate Simulations with Sparse Gaussian Processes. - Michael Grosskopf; Earl Lawrence; Ayan Biswas; Li Tang 007; Kelin Rumsey; Luke Van Roekel; Nathan M. Urban (ISAV@SC, 2021)

TributaryPCA: Distributed, Streaming PCA for In Situ Dimension Reduction with Application to Space Weather Simulations. - Yu Wang; Natalie Klein; Steven Morley; Vania Jordanova; Michael Henderson; Ayan Biswas; Earl Lawrence (DRBSD@SC, 2021)

An initial exploration of Bayesian model calibration for estimating the composition of rocks and soils on Mars. - Claire-Alice Hébert; Earl Lawrence; Kary L. Myers; James Colgan; Elizabeth J. Judge (Stat. Anal. Data Min., 2021)

A comparison of Gaussian processes and neural networks for computer model emulation and calibration. - Samuel Myren; Earl Lawrence (Stat. Anal. Data Min., 2021)

Probabilistic Data-Driven Sampling via Multi-Criteria Importance Analysis. - Ayan Biswas; Soumya Dutta; Earl Lawrence; John Patchett; Jon C. Calhoun; James P. Ahrens (IEEE Trans. Vis. Comput. Graph., 2021)

Fast emulation of density functional theory simulations using approximate Gaussian processes. - Steven Stetzler; Michael Grosskopf; Earl Lawrence (CoRR, 2022)

Scaled Vecchia Approximation for Fast Computer-Model Emulation. - Matthias Katzfuss; Joseph Guinness; Earl Lawrence (SIAM/ASA J. Uncertain. Quantification, 2022)

Temporal Characterization and Filtering of Sensor Data to Support Anomaly Detection. - Michael J. Grosskopf; Kary L. Myers; Earl Lawrence; Derek Bingham (Technometrics, 2022)

Dynamic Data Assimilation of MPAS-O and the Global Drifter Dataset. - Derek DeSantis; Ayan Biswas; Earl Lawrence; Phillip Wolfram (CoRR, 2023)

Filling the Void: Data-Driven Machine Learning-based Reconstruction of Sampled Spatiotemporal Scientific Simulation Data. - Ayan Biswas; Aditi Mishra; Meghanto Majumder; Subhashis Hazarika; Alexander Most; Juan Castorena; Christopher Bryan; Patrick S. McCormick; James P. Ahrens; Earl Lawrence; Aric A. Hagberg (SC Workshops, 2024)

URSA: The Universal Research and Scientific Agent. - Michael Grosskopf; Russell Bent; Rahul Somasundaram; Isaac J. Michaud; Arthur Lui; Nathan DeBardeleben; Earl Lawrence (CoRR, 2025)

A Partitioned Sparse Variational Gaussian Process for Fast, Distributed Spatial Modeling. - Michael Grosskopf; Kellin N. Rumsey; Ayan Biswas; Earl Lawrence (CoRR, 2025)

VizGenie: Toward Self-Refining, Domain-Aware Workflows for Next-Generation Scientific Visualization. - Ayan Biswas; Terece L. Turton; Nishath Rajiv Ranasinghe; Shawn M. Jones; Bradley Love; William M. Jones; Aric A. Hagberg; Han-Wei Shen; Nathan DeBardeleben; Earl Lawrence (CoRR, 2025)

A Foundation Model for Material Fracture Prediction. - Agnese Marcato; Aleksandra Pachalieva; Ryley G. Hill; Kai Gao; Xiaoyu Wang; Esteban Rougier; Zhou Lei; Vinamra Agrawal; Janel Chua; Qinjun Kang; Jeffrey D. Hyman; Abigail Hunter; et al. (CoRR, 2025)