Data-driven Discovery of Closure Models

25 March 2018

Papers citing "Data-driven Discovery of Closure Models"

11 / 11 papers shown

Title
Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations Anudhyan Boral Z. Y. Wan Leonardo Zepeda-Núñez James Lottes Qing Wang Yi-fan Chen John R. Anderson Fei Sha AI4CE PINN 35 11 0 01 Jun 2023
Generalized Neural Closure Models with Interpretability Abhinava Gupta Pierre FJ Lermusiaux AI4CE 30 9 0 15 Jan 2023
Deep Learning of Chaotic Systems from Partially-Observed Data V. Churchill D. Xiu 40 12 0 12 May 2022
Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders Joseph Bakarji Kathleen P. Champion J. Nathan Kutz Steven L. Brunton 40 82 0 13 Jan 2022
Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference N. Sawant Boris Kramer Benjamin Peherstorfer AI4CE 14 28 0 06 Jul 2021
Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories W. I. Uy Benjamin Peherstorfer OffRL 11 13 0 01 Mar 2021
Neural Closure Models for Dynamical Systems Abhinav Gupta Pierre FJ Lermusiaux AI4CE 27 45 0 27 Dec 2020
Discovery of Dynamics Using Linear Multistep Methods Rachael Keller Q. Du 31 36 0 29 Dec 2019
Tensor Basis Gaussian Process Models of Hyperelastic Materials A. Frankel Reese E. Jones L. Swiler 11 41 0 23 Dec 2019
Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability Shaowu Pan Karthik Duraisamy 23 136 0 09 Jun 2019
Long-time predictive modeling of nonlinear dynamical systems using neural networks Shaowu Pan Karthik Duraisamy 39 92 0 31 May 2018