1 code implementation • 8 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.
no code implementations • 15 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.
no code implementations • 26 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.
2 code implementations • 1 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.
no code implementations • 7 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.
no code implementations • 18 Sep 2019 • Romit Maulik, Vishwas Rao, Sandeep Madireddy, Bethany Lusch, Prasanna Balaprakash
Rapid simulations of advection-dominated problems are vital for multiple engineering and geophysical applications.
1 code implementation • 27 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.
1 code implementation • 13 Dec 2016 • Bethany Lusch, Jake Weholt, Pedro D. Maia, J. Nathan Kutz
However, we provide important insight and a quantitative framework for disorders in which FAS are implicated.
1 code implementation • 16 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.
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.