Picture for Brian K. Spears

Brian K. Spears

Lawrence Livermore National Laboratory, Livermore, CA

Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion

Add code
Nov 24, 2021
Figure 1 for Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion
Figure 2 for Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion
Figure 3 for Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion
Figure 4 for Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion
Viaarxiv icon

Transfer learning suppresses simulation bias in predictive models built from sparse, multi-modal data

Add code
Apr 19, 2021
Figure 1 for Transfer learning suppresses simulation bias in predictive models built from sparse, multi-modal data
Figure 2 for Transfer learning suppresses simulation bias in predictive models built from sparse, multi-modal data
Figure 3 for Transfer learning suppresses simulation bias in predictive models built from sparse, multi-modal data
Figure 4 for Transfer learning suppresses simulation bias in predictive models built from sparse, multi-modal data
Viaarxiv icon

Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations

Add code
Oct 26, 2020
Figure 1 for Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations
Figure 2 for Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations
Figure 3 for Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations
Figure 4 for Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations
Viaarxiv icon

Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies

Add code
Dec 17, 2019
Figure 1 for Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies
Figure 2 for Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies
Figure 3 for Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies
Figure 4 for Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies
Viaarxiv icon

Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion

Add code
Oct 03, 2019
Figure 1 for Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion
Figure 2 for Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion
Figure 3 for Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion
Viaarxiv icon

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications

Add code
Jul 19, 2019
Figure 1 for Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications
Figure 2 for Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications
Figure 3 for Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications
Figure 4 for Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications
Viaarxiv icon

Contemporary machine learning: a guide for practitioners in the physical sciences

Add code
Dec 20, 2017
Figure 1 for Contemporary machine learning: a guide for practitioners in the physical sciences
Figure 2 for Contemporary machine learning: a guide for practitioners in the physical sciences
Figure 3 for Contemporary machine learning: a guide for practitioners in the physical sciences
Figure 4 for Contemporary machine learning: a guide for practitioners in the physical sciences
Viaarxiv icon