Submodular Hamming Metrics. - Jennifer Gillenwater; Rishabh K. Iyer; Bethany Lusch; Rahul Kidambi; Jeff A. Bilmes (NIPS, 2015)

Shape Constrained Tensor Decompositions using Sparse Representations in Over-Complete Libraries. - Bethany Lusch; Eric C. Chi; J. Nathan Kutz (CoRR, 2016)

Deep learning for universal linear embeddings of nonlinear dynamics. - Bethany Lusch; J. Nathan Kutz; Steven L. Brunton (CoRR, 2017)

Shape Constrained Tensor Decompositions. - Bethany Lusch; Eric C. Chi; J. Nathan Kutz (DSAA, 2019)

MELA: A Visual Analytics Tool for Studying Multifidelity HPC System Logs. - Shilpika; Bethany Lusch; Murali Emani; Venkatram Vishwanath; Michael E. Papka; Kwan-Liu Ma (DAAC@SC, 2019)

Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models. - Romit Maulik; Vishwas Rao; Sandeep Madireddy; Bethany Lusch; Prasanna Balaprakash (CoRR, 2019)

Deep Learning Models for Global Coordinate Transformations that Linearize PDEs. - Craig Gin; Bethany Lusch; Steven L. Brunton; J. Nathan Kutz (CoRR, 2019)

Recurrent neural network architecture search for geophysical emulation. - Romit Maulik; Romain Egele; Bethany Lusch; Prasanna Balaprakash (SC, 2020)

Deploying deep learning in OpenFOAM with TensorFlow. - Romit Maulik; Himanshu Sharma; Saumil Patel; Bethany Lusch; Elise Jennings (CoRR, 2020)

Data-Driven Deep Learning Emulators for Geophysical Forecasting. - Varuni Katti Sastry; Romit Maulik; Vishwas Rao; Bethany Lusch; S. Ashwin Renganathan; Rao Kotamarthi (ICCS (5), 2021)

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification. - Romain Egele; Romit Maulik; Krishnan Raghavan; Prasanna Balaprakash; Bethany Lusch (CoRR, 2021)

Toward an In-Depth Analysis of Multifidelity High Performance Computing Systems. - Shilpika; Bethany Lusch; Murali Emani; Filippo Simini; Venkatram Vishwanath; Michael E. Papka; Kwan-Liu Ma (CCGRID, 2022)

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification. - Romain Egele; Romit Maulik; Krishnan Raghavan; Bethany Lusch; Isabelle Guyon; Prasanna Balaprakash (ICPR, 2022)

PythonFOAM: In-situ data analyses with OpenFOAM and Python. - Romit Maulik; Dimitrios Fytanidis; Bethany Lusch; Venkatram Vishwanath; Saumil Patel (J. Comput. Sci., 2022)

Computationally Efficient Data-Driven Discovery and Linear Representation of Nonlinear Systems for Control. - Madhur Tiwari; George Nehma; Bethany Lusch (IEEE Control. Syst. Lett., 2023)

A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of Multifidelity HPC Systems. - Shilpika; Bethany Lusch; Murali Emani; Filippo Simini; Venkatram Vishwanath; Michael E. Papka; Kwan-Liu Ma (CCGrid, 2024)

Scalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling. - Shivam Barwey; Riccardo Balin; Bethany Lusch; Saumil Patel; Ramesh Balakrishnan; Pinaki Pal; Romit Maulik; Venkatram Vishwanath (SC Workshops, 2024)

An Incremental Multi-Level, Multi-Scale Approach to Assessment of Multifidelity HPC Systems. - Shilpika; Bethany Lusch; Venkatram Vishwanath; Michael E. Papka (SC Workshops, 2024)

Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networks. - Shivam Barwey; Pinaki Pal; Saumil Patel; Riccardo Balin; Bethany Lusch; Venkatram Vishwanath; Romit Maulik; Ramesh Balakrishnan (CoRR, 2024)