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

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

9 June 2021

Steven L. Brunton

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

9 / 9 papers shown

Title
RandONet: Shallow-Networks with Random Projections for learning linear and nonlinear operators Gianluca Fabiani Ioannis G. Kevrekidis Constantinos Siettos A. Yannacopoulos 112 13 0 08 Jun 2024
Discovering Efficient Periodic Behaviours in Mechanical Systems via Neural Approximators Yannik P. Wotte Sven Dummer N. Botteghi C. Brune Stefano Stramigioli Federico Califano 78 6 0 29 Dec 2022
Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions Paolo Conti G. Gobat S. Fresca Andrea Manzoni A. Frangi AI4CE 90 51 0 13 Nov 2022
Data-driven reduced order models using invariant foliations, manifolds and autoencoders R. Szalai AI4CE 69 10 0 24 Jun 2022
Dimensionally Consistent Learning with Buckingham Pi Joseph Bakarji Jared L. Callaham Steven L. Brunton N. Kutz 64 41 0 09 Feb 2022
Machine Learning in Heterogeneous Porous Materials Martha DÉli H. Deng Cedric G. Fraces K. Garikipati L. Graham‐Brady ... H. Tchelepi B. Važić Hari S. Viswanathan H. Yoon P. Zarzycki AI4CE 80 9 0 04 Feb 2022
Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders Joseph Bakarji Kathleen P. Champion J. Nathan Kutz Steven L. Brunton 111 86 0 13 Jan 2022
A toolkit for data-driven discovery of governing equations in high-noise regimes Charles B. Delahunt J. Nathan Kutz 107 19 0 08 Nov 2021
An artificial neural network approach to bifurcating phenomena in computational fluid dynamics F. Pichi F. Ballarin G. Rozza J. Hesthaven AI4CE 85 73 0 22 Sep 2021