Search Results for author: Brian K. Spears

Found 7 papers, 3 papers with code

Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion

no code implementations24 Nov 2021 Ankita Shukla, Rushil Anirudh, Eugene Kur, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Brian K. Spears, Tammy Ma, Pavan Turaga

In this paper, we develop a Wasserstein autoencoder (WAE) with a hyperspherical prior for multimodal data in the application of inertial confinement fusion.

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

no code implementations19 Apr 2021 Bogdan Kustowski, Jim A. Gaffney, Brian K. Spears, Gemma J. Anderson, Rushil Anirudh, Peer-Timo Bremer, Jayaraman J. Thiagarajan

The transfer learning method can be applied to other problems that require transferring knowledge from simulations to the domain of real observations.

Transfer Learning

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

no code implementations26 Oct 2020 Gemma J. Anderson, Jim A. Gaffney, Brian K. Spears, Peer-Timo Bremer, Rushil Anirudh, Jayaraman J. Thiagarajan

Large-scale numerical simulations are used across many scientific disciplines to facilitate experimental development and provide insights into underlying physical processes, but they come with a significant computational cost.

Variational Inference

Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies

1 code implementation17 Dec 2019 Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Brian K. Spears

Neural networks have become very popular in surrogate modeling because of their ability to characterize arbitrary, high dimensional functions in a data driven fashion.

Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion

2 code implementations3 Oct 2019 Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu, Peer-Timo Bremer, Brian K. Spears

There is significant interest in using modern neural networks for scientific applications due to their effectiveness in modeling highly complex, non-linear problems in a data-driven fashion.

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

no code implementations20 Dec 2017 Brian K. Spears

We finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help.

Time Series

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