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Deep learning for universal linear embeddings of nonlinear dynamics

Nature communications, 2018-11, Vol.9 (1), p.4950-10, Article 4950

2018

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Data-driven discovery of coordinates and governing equations

Proceedings of the National Academy of Sciences - PNAS, 2019-11, Vol.116 (45), p.22445-22451

2019

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Time-series learning of latent-space dynamics for reduced-order model closure

Physica. D, 2020-04, Vol.405 (C), p.132368, Article 132368

2020

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A turbulent eddy-viscosity surrogate modeling framework for Reynolds-averaged Navier-Stokes simulations

Computers & fluids, 2021-09, Vol.227, p.104777, Article 104777

2021

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Deep learning models for global coordinate transformations that linearise PDEs

European journal of applied mathematics, 2021-06, Vol.32 (3), p.515-539

2021

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

IEEE control systems letters, 2023-01, Vol.7, p.1-1

2023

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Efficient high-dimensional variational data assimilation with machine-learned reduced-order models

Geoscientific Model Development, 2022-05, Vol.15 (8), p.3433-3445

2022

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PythonFOAM: In-situ data analyses with OpenFOAM and Python

Journal of computational science, 2022-07, Vol.62, p.101750, Article 101750

2022

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AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification

2022 26th International Conference on Pattern Recognition (ICPR), 2022, p.1908-1914

2022

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Modeling cognitive deficits following neurodegenerative diseases and traumatic brain injuries with deep convolutional neural networks

Brain and cognition, 2018-06, Vol.123, p.154-164

2018

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Artificial Intelligence Guided Studies of van der Waals Magnets

Advanced theory and simulations, 2023-06, Vol.6 (6), p.n/a

2023

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Artificial Intelligence Guided Studies of van der Waals Magnets (Adv. Theory Simul. 6/2023)

Advanced theory and simulations, 2023-06, Vol.6 (6), p.n/a

2023

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MELA: A Visual Analytics Tool for Studying Multifidelity HPC System Logs

2019 IEEE/ACM Industry/University Joint International Workshop on Data-center Automation, Analytics, and Control (DAAC), 2019, p.13-18

2019

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Inferring connectivity in networked dynamical systems: Challenges using Granger causality

Physical review. E, 2016-09, Vol.94 (3-1), p.032220-032220, Article 032220

2016

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Machine learning and data decompositions for complex networked dynamical systems

Lusch, Bethany

2016

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Data-Driven Deep Learning Emulators for Geophysical Forecasting

Computational Science – ICCS 2021, p.433-446

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Recurrent Neural Network Architecture Search for Geophysical Emulation

SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, 2020, p.1-14

2020

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Recurrent neural network architecture search for geophysical emulation

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2020, p.1-14

2020

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Shape Constrained Tensor Decompositions

2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2019, p.287-297

2019

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Toward an In-Depth Analysis of Multifidelity High Performance Computing Systems

2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), 2022, p.716-725

2022

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Deep learning for universal linear embeddings of nonlinear dynamics

Data-driven discovery of coordinates and governing equations

Time-series learning of latent-space dynamics for reduced-order model closure

A turbulent eddy-viscosity surrogate modeling framework for Reynolds-averaged Navier-Stokes simulations

Deep learning models for global coordinate transformations that linearise PDEs

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

Efficient high-dimensional variational data assimilation with machine-learned reduced-order models

PythonFOAM: In-situ data analyses with OpenFOAM and Python

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification

Modeling cognitive deficits following neurodegenerative diseases and traumatic brain injuries with deep convolutional neural networks

Artificial Intelligence Guided Studies of van der Waals Magnets

Artificial Intelligence Guided Studies of van der Waals Magnets (Adv. Theory Simul. 6/2023)

MELA: A Visual Analytics Tool for Studying Multifidelity HPC System Logs

Inferring connectivity in networked dynamical systems: Challenges using Granger causality

Machine learning and data decompositions for complex networked dynamical systems

Data-Driven Deep Learning Emulators for Geophysical Forecasting

Recurrent Neural Network Architecture Search for Geophysical Emulation

Recurrent neural network architecture search for geophysical emulation

Shape Constrained Tensor Decompositions

Toward an In-Depth Analysis of Multifidelity High Performance Computing Systems

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