J. Nathan Kutz

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

∙ 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

∙ 11/19/2022

Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants

Recent progress in autoencoder-based sparse identification of nonlinear ...

0 L. Mars Gao, 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

∙ 09/18/2022

Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series Data

In many scenarios, it is necessary to monitor a complex system via a tim...

50 Alex Mallen, 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

∙ 04/07/2022

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

∙ 03/10/2022

Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effects

Physics-based and first-principles models pervade the engineering and ph...

9 Megan R. Ebers, 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

∙ 01/04/2022

A new approach for determining optimal placement of PM2.5 air quality sensors: case study for the contiguous United States

Considerable financial resources are allocated for measuring ambient air...

0 Makoto M. Kelp, 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

∙ 11/08/2021

A toolkit for data-driven discovery of governing equations in high-noise regimes

We consider the data-driven discovery of governing equations from time-s...

19 Charles B. Delahunt, et al. ∙

research

∙ 10/29/2021

FC2T2: The Fast Continuous Convolutional Taylor Transform with Applications in Vision and Graphics

Series expansions have been a cornerstone of applied mathematics and eng...

1 Henning Lange, et al. ∙

research

∙ 08/16/2021

Robust Trimmed k-means

Clustering is a fundamental tool in unsupervised learning, used to group...

9 Olga Dorabiala, et al. ∙

research

∙ 07/22/2021

Bagging, optimized dynamic mode decomposition (BOP-DMD) for robust, stable forecasting with spatial and temporal uncertainty-quantification

Dynamic mode decomposition (DMD) provides a regression framework for ada...

5 Diya Sashidhar, et al. ∙

research

∙ 06/10/2021

Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties

Probabilistic forecasting of complex phenomena is paramount to various s...

19 Alex Mallen, 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

∙ 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

∙ 10/08/2020

Dynamic mode decomposition for forecasting and analysis of power grid load data

Time series forecasting remains a central challenge problem in almost al...

0 Daniel Dylewsky, 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

∙ 06/23/2020

Inferring Causal Networks of Dynamical Systems through Transient Dynamics and Perturbation

Inferring causal relations from time series measurements is an ill-posed...

0 George Stepaniants, 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

∙ 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

∙ 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

∙ 03/25/2019

Dynamic mode decomposition for multiscale nonlinear physics

We present a data-driven method for separating complex, multiscale syste...

0 Daniel Dylewsky, et al. ∙

research

∙ 03/25/2019

Data-driven multiscale decompositions for forecasting and model discovery

We present a data-driven method for separating complex, multiscale syste...

0 Daniel Dylewsky, 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

∙ 01/26/2019

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...

22 Charles B. Delahunt, 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

∙ 08/23/2018

Insect cyborgs: Biological feature generators improve machine learning accuracy on limited data

Despite many successes, machine learning (ML) methods such as neural net...

0 Charles B. Delahunt, et al. ∙

research

∙ 07/14/2018

Sparse Relaxed Regularized Regression: SR3

Regularized regression problems are ubiquitous in statistical modeling, ...

0 Travis Askham, 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

∙ 02/15/2018

Putting a bug in ML: The moth olfactory network learns to read MNIST

We seek to (i) characterize the learning architectures exploited in biol...

0 Charles B. Delahunt, et al. ∙

research

∙ 02/08/2018

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...

0 Charles B. Delahunt, 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

∙ 11/06/2017

Randomized Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) is a powerful tool for data minin...

0 N. Benjamin Erichson, et al. ∙

J. Nathan Kutz

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