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06/22/2023
PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator
PyKoopman is a Python package for the data-driven approximation of the K...
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04/06/2023
Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning
Finite-time Lyapunov exponents (FTLEs) provide a powerful approach to co...
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03/30/2023
Machine Learning for Partial Differential Equations
Partial differential equations (PDEs) are among the most universal and p...
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01/25/2023
Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning
We present a convolutional framework which significantly reduces the com...
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11/12/2021
PySINDy: A comprehensive Python package for robust sparse system identification
Automated data-driven modeling, the process of directly discovering the ...
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10/05/2021
The Potential of Machine Learning to Enhance Computational Fluid Dynamics
Machine learning is rapidly becoming a core technology for scientific co...
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04/10/2020
Multiresolution Convolutional Autoencoders
We propose a multi-resolution convolutional autoencoder (MrCAE) architec...
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11/07/2019
Deep Learning Models for Global Coordinate Transformations that Linearize PDEs
We develop a deep autoencoder architecture that can be used to find a co...
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09/18/2019
Learning Discrepancy Models From Experimental Data
First principles modeling of physical systems has led to significant tec...
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04/01/2018
Sparse Principal Component Analysis via Variable Projection
Sparse principal component analysis (SPCA) has emerged as a powerful tec...
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02/23/2018
Diffusion Maps meet Nyström
Diffusion maps are an emerging data-driven technique for non-linear dime...
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12/27/2017
Deep learning for universal linear embeddings of nonlinear dynamics
Identifying coordinate transformations that make strongly nonlinear dyna...
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03/27/2017
