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09/01/2023
Multi-fidelity reduced-order surrogate modeling
High-fidelity numerical simulations of partial differential equations (P...
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08/08/2023
Nonlinear parametric models of viscoelastic fluid flows
Reduced-order models have been widely adopted in fluid mechanics, partic...
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06/23/2023
Optimal Sensor Placement with Adaptive Constraints for Nuclear Digital Twins
Given harsh operating conditions and physical constraints in reactors, n...
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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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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 ...
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01/30/2023
Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery
Sparse model identification enables nonlinear dynamical system discovery...
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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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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...
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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...
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04/08/2022
Swarm Modelling with Dynamic Mode Decomposition
Modelling biological or engineering swarms is challenging due to the inh...
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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-...
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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 ...
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02/09/2022
Dimensionally Consistent Learning with Buckingham Pi
In the absence of governing equations, dimensional analysis is a robust ...
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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...
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12/08/2021
Physics-informed dynamic mode decomposition (piDMD)
In this work, we demonstrate how physical principles – such as symmetrie...
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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...
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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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10/28/2021
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...
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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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10/05/2021
Applying Machine Learning to Study Fluid Mechanics
This paper provides a short overview of how to use machine learning to b...
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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...
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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...
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04/01/2021
Deep Learning of Conjugate Mappings
Despite many of the most common chaotic dynamical systems being continuo...
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03/18/2021
Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows
Intelligent mobile sensors, such as uninhabited aerial or underwater veh...
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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...
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02/24/2021
Modern Koopman Theory for Dynamical Systems
The field of dynamical systems is being transformed by the mathematical ...
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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...
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08/24/2020
Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning
Data science, and machine learning in particular, is rapidly transformin...
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08/22/2020
Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers
Nonlinear differential equations rarely admit closed-form solutions, thu...
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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...
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04/10/2020
Multiresolution Convolutional Autoencoders
We propose a multi-resolution convolutional autoencoder (MrCAE) architec...
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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 ...
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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...
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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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09/02/2019
Randomized methods to characterize large-scale vortical flow network
We demonstrate the effective use of randomized methods for linear algebr...
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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...
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06/19/2019
Discovery of Physics from Data: Universal Laws and Discrepancy Models
Machine learning (ML) and artificial intelligence (AI) algorithms are no...
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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...
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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,...
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11/28/2018
RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology
Retinal template matching and registration is an important challenge in ...
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11/02/2018
Discovering conservation laws from data for control
Conserved quantities, i.e. constants of motion, are critical for charact...
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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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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 ...
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03/27/2017
Randomized CP Tensor Decomposition
The CANDECOMP/PARAFAC (CP) tensor decomposition is a popular dimensional...
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02/07/2017
