Publications

Romit Maulik, Himanshu Sharma, Saumil Patel, Bethany Lusch, Elise Jennings (2020). Accelerating RANS Simulations Using A Data-Driven Framework for Eddy-Viscosity Emulation.

PDF

Romit Maulik, Romain Egele, Bethany Lusch, Prasanna Balaprakash (2020). Recurrent Neural Network Architecture Search for Geophysical Emulation.

PDF

Romit Maulik, Bethany Lusch, Prasanna Balaprakash (2020). Reduced-order modeling of advection-dominatedsystems with recurrent neural networks andconvolutional autoencoders.

PDF

Romit Maulik, Arvind Mohan, Bethany Lusch, Sandeep Madireddy, Prasanna Balaprakash, Daniel Livescu (2020). Time-series learning of latent-space dynamics for reduced-order model closure. Physica D: Nonlinear Phenomena.

PDF

F.N.U. Shilpika, Bethany Lusch, Murali Emani, Venkatram Vishwanath, Michael E. Papka, Kwan Liu Ma (2019). MELA: A visual analytics tool for studying multifidelity HPC system logs. Proceedings of DAAC 2019.

Source Document

Criag Gin, Bethany Lusch, Steven L. Brunton, J. Nathan Kutz (2019). Deep Learning Models for Global CoordinateTransformations that Linearize PDEs.

PDF

Romit Maulik, Vishwas Rao, Sandeep Madireddy, Bethany Lusch, Prasanna Balaprakash (2019). Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models. Second Workshop on Machine Learning and the Physical Sciences at NeurIPS.

PDF

Kathleen Champion, Bethany Lusch, J. Nathan Kutz, Steven L. Brunton (2019). Data-driven discovery of coordinates andgoverning equations. PNAS.

PDF

Bethany Lusch, J. Nathan Kutz, Steven L. Brunton (2018). Deep learning for universal linear embeddings of nonlinear dynamics. Nature Communications.

PDF

Bethany Lusch, Jake Weholt, Pedro Maia, J. Nathan Kutz (2016). Modeling cognitive deficits following neurodegenerative diseases and traumatic brain injuries with deep convolutional neural networks. Brain and Cognition.

Source Document

Bethany Lusch, Pedro D. Maia, J. Nathan Kutz (2016). Inferring Connectivity in Networked Dynamical Systems: Challenges Using Granger Causality. Physical Review.

Source Document

Bethany Lusch, Eric C. Chi, J. Nathan Kutz (2016). Shape Constrained Tensor Decompositions usingSparse Representations in Over-Complete Libraries. DSAA.

PDF

Jennifer Gillenwater, Rishabh Iyer, Bethany Lusch, Rahul Kidambi, Jeff Bilmes (2015). Submodular Hamming Metrics. Advances in Neural Information Processing Systems.

PDF