Search Results for author: Bethany Lusch

Found 10 papers, 5 papers with code

Computationally Efficient Data-Driven Discovery and Linear Representation of Nonlinear Systems For Control

1 code implementation8 Sep 2023 Madhur Tiwari, George Nehma, Bethany Lusch

This work focuses on developing a data-driven framework using Koopman operator theory for system identification and linearization of nonlinear systems for control.

A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of Multifidelity HPC Systems

no code implementations15 Jun 2023 Shilpika, Bethany Lusch, Murali Emani, Filippo Simini, Venkatram Vishwanath, Michael E. Papka, Kwan-Liu Ma

This end-to-end log analysis system, coupled with visual analytics support, allows users to glean and promptly extract supercomputer usage and error patterns at varying temporal and spatial resolutions.

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification

no code implementations26 Oct 2021 Romain Egele, Romit Maulik, Krishnan Raghavan, Bethany Lusch, Isabelle Guyon, Prasanna Balaprakash

However, building ensembles of neural networks is a challenging task because, in addition to choosing the right neural architecture or hyperparameters for each member of the ensemble, there is an added cost of training each model.

Uncertainty Quantification

Deploying deep learning in OpenFOAM with TensorFlow

2 code implementations1 Dec 2020 Romit Maulik, Himanshu Sharma, Saumil Patel, Bethany Lusch, Elise Jennings

We outline the development of a data science module within OpenFOAM which allows for the in-situ deployment of trained deep learning architectures for general-purpose predictive tasks.

BIG-bench Machine Learning

Deep Learning Models for Global Coordinate Transformations that Linearize PDEs

no code implementations7 Nov 2019 Craig Gin, Bethany Lusch, Steven L. Brunton, J. Nathan Kutz

By leveraging a residual network architecture, a near-identity transformation can be exploited to encode intrinsic coordinates in which the dynamics are linear.

Deep learning for universal linear embeddings of nonlinear dynamics

1 code implementation27 Dec 2017 Bethany Lusch, J. Nathan Kutz, Steven L. Brunton

Identifying coordinate transformations that make strongly nonlinear dynamics approximately linear is a central challenge in modern dynamical systems.

Shape Constrained Tensor Decompositions using Sparse Representations in Over-Complete Libraries

1 code implementation16 Aug 2016 Bethany Lusch, Eric C. Chi, J. Nathan Kutz

We consider $N$-way data arrays and low-rank tensor factorizations where the time mode is coded as a sparse linear combination of temporal elements from an over-complete library.

Tensor Decomposition

Submodular Hamming Metrics

no code implementations NeurIPS 2015 Jennifer Gillenwater, Rishabh Iyer, Bethany Lusch, Rahul Kidambi, Jeff Bilmes

We show that there is a largely unexplored class of functions (positive polymatroids) that can define proper discrete metrics over pairs of binary vectors and that are fairly tractable to optimize over.

Clustering

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