Search Results for author: Earl Lawrence

Found 5 papers, 2 papers with code

Dynamic Data Assimilation of MPAS-O and the Global Drifter Dataset

no code implementations11 Jan 2023 Derek DeSantis, Ayan Biswas, Earl Lawrence, Phillip Wolfram

In this study, we propose a new method for combining in situ buoy measurements with Earth system models (ESMs) to improve the accuracy of temperature predictions in the ocean.

Fast emulation of density functional theory simulations using approximate Gaussian processes

no code implementations24 Aug 2022 Steven Stetzler, Michael Grosskopf, Earl Lawrence

This work examines the accuracy-runtime trade-off of several approximate Gaussian process models -- the sparse variational GP, stochastic variational GP, and deep kernel learned GP -- when emulating the predictions of density functional theory (DFT) models.

Gaussian Processes

Relationship-aware Multivariate Sampling Strategy for Scientific Simulation Data

no code implementations31 Aug 2020 Subhashis Hazarika, Ayan Biswas, Phillip J. Wolfram, Earl Lawrence, Nathan Urban

With the increasing computational power of current supercomputers, the size of data produced by scientific simulations is rapidly growing.

Scaled Vecchia approximation for fast computer-model emulation

1 code implementation1 May 2020 Matthias Katzfuss, Joseph Guinness, Earl Lawrence

Many scientific phenomena are studied using computer experiments consisting of multiple runs of a computer model while varying the input settings.

Gaussian Processes

The Mira-Titan Universe: Precision Predictions for Dark Energy Surveys

1 code implementation11 Aug 2015 Katrin Heitmann, Derek Bingham, Earl Lawrence, Steven Bergner, Salman Habib, David Higdon, Adrian Pope, Rahul Biswas, Hal Finkel, Nicholas Frontiere, Suman Bhattacharya

The new sampling method allows us to build precision emulators from just 26 cosmological models and to increase the emulator accuracy by adding new sets of simulations in a prescribed way.

Cosmology and Nongalactic Astrophysics

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