
Dynamic mode decomposition for forecasting and analysis of power grid load data
Time series forecasting remains a central challenge problem in almost al...
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DataDriven Aerospace Engineering: Reframing the Industry with Machine Learning
Data science, and machine learning in particular, is rapidly transformin...
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Hierarchical Deep Learning of Multiscale Differential Equation TimeSteppers
Nonlinear differential equations rarely admit closedform solutions, thu...
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Inferring Causal Networks of Dynamical Systems through Transient Dynamics and Perturbation
Inferring causal relations from time series measurements is an illposed...
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SINDyBVP: Sparse Identification of Nonlinear Dynamics for Boundary Value Problems
We develop a datadriven model discovery and system identification techn...
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Multiresolution Convolutional Autoencoders
We propose a multiresolution convolutional autoencoder (MrCAE) architec...
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SINDyPI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics
Accurately modeling the nonlinear dynamics of a system from measurement ...
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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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Learning Discrepancy Models From Experimental Data
First principles modeling of physical systems has led to significant tec...
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A unified sparse optimization framework to learn parsimonious physicsinformed models from data
Machine learning (ML) is redefining what is possible in dataintensive f...
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Discovery of Physics from Data: Universal Laws and Discrepancy Models
Machine learning (ML) and artificial intelligence (AI) algorithms are no...
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Deep Model Predictive Control with Online Learning for Complex Physical Systems
The control of complex systems is of critical importance in many branche...
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Dynamic mode decomposition for multiscale nonlinear physics
We present a datadriven method for separating complex, multiscale syste...
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Datadriven multiscale decompositions for forecasting and model discovery
We present a datadriven method for separating complex, multiscale syste...
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Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data
In many applications, it is important to reconstruct a fluid flow field,...
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Money on the Table: Statistical information ignored by Softmax can improve classifier accuracy
Softmax is a standard final layer used in Neural Nets (NNs) to summarize...
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Discovering conservation laws from data for control
Conserved quantities, i.e. constants of motion, are critical for charact...
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Insect cyborgs: Biological feature generators improve machine learning accuracy on limited data
Despite many successes, machine learning (ML) methods such as neural net...
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Sparse Relaxed Regularized Regression: SR3
Regularized regression problems are ubiquitous in statistical modeling, ...
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Sparse Principal Component Analysis via Variable Projection
Sparse principal component analysis (SPCA) has emerged as a powerful tec...
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Diffusion Maps meet Nyström
Diffusion maps are an emerging datadriven technique for nonlinear dime...
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Putting a bug in ML: The moth olfactory network learns to read MNIST
We seek to (i) characterize the learning architectures exploited in biol...
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Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, with Applications to Neural Nets
The insect olfactory system, which includes the antennal lobe (AL), mush...
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Deep learning for universal linear embeddings of nonlinear dynamics
Identifying coordinate transformations that make strongly nonlinear dyna...
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Predicting shim gaps in aircraft assembly with machine learning and sparse sensing
A modern aircraft may require on the order of thousands of custom shims ...
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Randomized Nonnegative Matrix Factorization
Nonnegative matrix factorization (NMF) is a powerful tool for data minin...
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Randomized CP Tensor Decomposition
The CANDECOMP/PARAFAC (CP) tensor decomposition is a popular dimensional...
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Randomized Dynamic Mode Decomposition
This paper presents a randomized algorithm for computing the nearoptima...
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Shape Constrained Tensor Decompositions using Sparse Representations in OverComplete Libraries
We consider Nway data arrays and lowrank tensor factorizations where t...
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Randomized Matrix Decompositions using R
Matrix decompositions are fundamental tools in the area of applied mathe...
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Compressed Dynamic Mode Decomposition for Background Modeling
We introduce the method of compressed dynamic mode decomposition (cDMD) ...
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Selecting a Small Set of Optimal Gestures from an Extensive Lexicon
Finding the best set of gestures to use for a given computer recognition...
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Dynamic Mode Decomposition for RealTime Background/Foreground Separation in Video
This paper introduces the method of dynamic mode decomposition (DMD) for...
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J. Nathan Kutz
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Robert Bolles and Yasuko Endo Professor, Adjunct Professor of Electrical Engineering and Physics