
Learning normal form autoencoders for datadriven discovery of universal,parameterdependent governing equations
Complex systems manifest a small number of instabilities and bifurcation...
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Extraction of instantaneous frequencies and amplitudes in nonstationary timeseries data
Timeseries analysis is critical for a diversity of applications in scie...
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Deep Learning of Conjugate Mappings
Despite many of the most common chaotic dynamical systems being continuo...
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FiniteHorizon, EnergyOptimal Trajectories in Unsteady Flows
Intelligent mobile sensors, such as uninhabited aerial or underwater veh...
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Projectiontree reduced order modeling for fast Nbody computations
This work presents a datadriven reducedorder modeling framework to acc...
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Modern Koopman Theory for Dynamical Systems
The field of dynamical systems is being transformed by the mathematical ...
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DeepGreen: Deep Learning of Green's Functions for Nonlinear Boundary Value Problems
Boundary value problems (BVPs) play a central role in the mathematical a...
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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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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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From Fourier to Koopman: Spectral Methods for Longterm Time Series Prediction
We propose spectral methods for longterm forecasting of temporal signal...
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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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Randomized methods to characterize largescale vortical flow network
We demonstrate the effective use of randomized methods for linear algebr...
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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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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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RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology
Retinal template matching and registration is an important challenge in ...
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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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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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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 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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SparseTDA: Sparse Realization of Topological Data Analysis for MultiWay Classification
Topological data analysis (TDA) has emerged as one of the most promising...
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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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Steven L. Brunton
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