Steven L. Brunton

research

∙ 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...

0 Kartik Krishna, et al. ∙

research

∙ 03/30/2023

Machine Learning for Partial Differential Equations

Partial differential equations (PDEs) are among the most universal and p...

3 Steven L. Brunton, et al. ∙

research

∙ 03/28/2023

The transformative potential of machine learning for experiments in fluid mechanics

The field of machine learning has rapidly advanced the state of the art ...

2 Ricardo Vinuesa, et al. ∙

research

∙ 01/30/2023

Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery

Sparse model identification enables nonlinear dynamical system discovery...

3 L. Mars Gao, et al. ∙

research

∙ 01/25/2023

Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning

We present a convolutional framework which significantly reduces the com...

135 Sebastian Peitz, et al. ∙

research

∙ 09/20/2022

Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC

Accurate and agile trajectory tracking in sub-gram Micro Aerial Vehicles...

11 Andrea Tagliabue, et al. ∙

research

∙ 05/12/2022

The Experimental Multi-Arm Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Control

The single, double, and triple pendulum has served as an illustrative ex...

0 Kadierdan Kaheman, et al. ∙

research

∙ 04/08/2022

Swarm Modelling with Dynamic Mode Decomposition

Modelling biological or engineering swarms is challenging due to the inh...

89 Emma Hansen, et al. ∙

research

∙ 04/07/2022

Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data

High-dimensional spatio-temporal dynamics can often be encoded in a low-...

124 Shaowu Pan, et al. ∙

research

∙ 02/11/2022

Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning

The complexity of real-world geophysical systems is often compounded by ...

9 Said Ouala, et al. ∙

research

∙ 02/09/2022

Dimensionally Consistent Learning with Buckingham Pi

In the absence of governing equations, dimensional analysis is a robust ...

77 Joseph Bakarji, et al. ∙

research

∙ 01/13/2022

Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders

A central challenge in data-driven model discovery is the presence of hi...

69 Joseph Bakarji, et al. ∙

research

∙ 12/08/2021

Physics-informed dynamic mode decomposition (piDMD)

In this work, we demonstrate how physical principles – such as symmetrie...

0 Peter J. Baddoo, et al. ∙

research

∙ 11/22/2021

Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control

Sparse model identification enables the discovery of nonlinear dynamical...

0 Urban Fasel, et al. ∙

research

∙ 11/12/2021

PySINDy: A comprehensive Python package for robust sparse system identification

Automated data-driven modeling, the process of directly discovering the ...

162 Alan A. Kaptanoglu, et al. ∙

research

∙ 10/28/2021

Deeptime: a Python library for machine learning dynamical models from time series data

Generation and analysis of time-series data is relevant to many quantita...

323 Moritz Hoffmann, et al. ∙

research

∙ 10/05/2021

The Potential of Machine Learning to Enhance Computational Fluid Dynamics

Machine learning is rapidly becoming a core technology for scientific co...

184 Ricardo Vinuesa, et al. ∙

research

∙ 10/05/2021

Applying Machine Learning to Study Fluid Mechanics

This paper provides a short overview of how to use machine learning to b...

46 Steven L. Brunton, et al. ∙

research

∙ 06/09/2021

Learning normal form autoencoders for data-driven discovery of universal,parameter-dependent governing equations

Complex systems manifest a small number of instabilities and bifurcation...

16 Manu Kalia, et al. ∙

research

∙ 04/03/2021

Extraction of instantaneous frequencies and amplitudes in nonstationary time-series data

Time-series analysis is critical for a diversity of applications in scie...

26 Daniel E. Shea, et al. ∙

research

∙ 04/01/2021

Deep Learning of Conjugate Mappings

Despite many of the most common chaotic dynamical systems being continuo...

3 Jason J. Bramburger, et al. ∙

research

∙ 03/18/2021

Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows

Intelligent mobile sensors, such as uninhabited aerial or underwater veh...

0 Kartik Krishna, et al. ∙

research

∙ 03/02/2021

Projection-tree reduced order modeling for fast N-body computations

This work presents a data-driven reduced-order modeling framework to acc...

0 Steven N. Rodriguez, et al. ∙

research

∙ 02/24/2021

Modern Koopman Theory for Dynamical Systems

The field of dynamical systems is being transformed by the mathematical ...

14 Steven L. Brunton, et al. ∙

research

∙ 12/31/2020

DeepGreen: Deep Learning of Green's Functions for Nonlinear Boundary Value Problems

Boundary value problems (BVPs) play a central role in the mathematical a...

1 Craig R. Gin, et al. ∙

research

∙ 08/24/2020

Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning

Data science, and machine learning in particular, is rapidly transformin...

0 Steven L. Brunton, et al. ∙

research

∙ 08/22/2020

Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers

Nonlinear differential equations rarely admit closed-form solutions, thu...

0 Yuying Liu, et al. ∙

research

∙ 05/19/2020

SINDy-BVP: Sparse Identification of Nonlinear Dynamics for Boundary Value Problems

We develop a data-driven model discovery and system identification techn...

1 Daniel E. Shea, et al. ∙

research

∙ 04/10/2020

Multiresolution Convolutional Autoencoders

We propose a multi-resolution convolutional autoencoder (MrCAE) architec...

8 Yuying Liu, et al. ∙

research

∙ 04/05/2020

SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics

Accurately modeling the nonlinear dynamics of a system from measurement ...

5 Kadierdan Kaheman, et al. ∙

research

∙ 04/01/2020

From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction

We propose spectral methods for long-term forecasting of temporal signal...

0 Henning Lange, et al. ∙

research

∙ 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...

34 Craig Gin, et al. ∙

research

∙ 09/18/2019

Learning Discrepancy Models From Experimental Data

First principles modeling of physical systems has led to significant tec...

34 Kadierdan Kaheman, et al. ∙

research

∙ 09/02/2019

Randomized methods to characterize large-scale vortical flow network

We demonstrate the effective use of randomized methods for linear algebr...

0 Zhe Bai, et al. ∙

research

∙ 06/25/2019

A unified sparse optimization framework to learn parsimonious physics-informed models from data

Machine learning (ML) is redefining what is possible in data-intensive f...

0 Kathleen Champion, et al. ∙

research

∙ 06/19/2019

Discovery of Physics from Data: Universal Laws and Discrepancy Models

Machine learning (ML) and artificial intelligence (AI) algorithms are no...

4 Brian de Silva, et al. ∙

research

∙ 05/24/2019

Deep Model Predictive Control with Online Learning for Complex Physical Systems

The control of complex systems is of critical importance in many branche...

0 Katharina Bieker, et al. ∙

research

∙ 02/20/2019

Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data

In many applications, it is important to reconstruct a fluid flow field,...

0 N. Benjamin Erichson, et al. ∙

research

∙ 11/28/2018

RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology

Retinal template matching and registration is an important challenge in ...

6 Chen gong, et al. ∙

research

∙ 11/02/2018

Discovering conservation laws from data for control

Conserved quantities, i.e. constants of motion, are critical for charact...

0 Eurika Kaiser, et al. ∙

research

∙ 04/01/2018

Sparse Principal Component Analysis via Variable Projection

Sparse principal component analysis (SPCA) has emerged as a powerful tec...

0 N. Benjamin Erichson, et al. ∙

research

∙ 02/23/2018

Diffusion Maps meet Nyström

Diffusion maps are an emerging data-driven technique for non-linear dime...

0 N. Benjamin Erichson, et al. ∙

research

∙ 12/27/2017

Deep learning for universal linear embeddings of nonlinear dynamics

Identifying coordinate transformations that make strongly nonlinear dyna...

0 Bethany Lusch, et al. ∙

research

∙ 11/24/2017

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 ...

0 Krithika Manohar, et al. ∙

research

∙ 03/27/2017

Randomized CP Tensor Decomposition

The CANDECOMP/PARAFAC (CP) tensor decomposition is a popular dimensional...

0 N. Benjamin Erichson, et al. ∙

research

∙ 02/07/2017

Randomized Dynamic Mode Decomposition

This paper presents a randomized algorithm for computing the near-optima...

0 N. Benjamin Erichson, et al. ∙

research

∙ 01/12/2017

Sparse-TDA: Sparse Realization of Topological Data Analysis for Multi-Way Classification

Topological data analysis (TDA) has emerged as one of the most promising...

0 Wei Guo, et al. ∙

research

∙ 08/06/2016

Randomized Matrix Decompositions using R

Matrix decompositions are fundamental tools in the area of applied mathe...

0 N. Benjamin Erichson, et al. ∙

research

∙ 12/14/2015

Compressed Dynamic Mode Decomposition for Background Modeling

We introduce the method of compressed dynamic mode decomposition (cDMD) ...

0 N. Benjamin Erichson, et al. ∙

Steven L. Brunton

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