Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
Maulik, Romit
Garland, Nathan A.
Burby, Joshua W.
Tang, Xian-Zhu
and
Balaprakash, Prasanna
2020.
Neural network representability of fully ionized plasma fluid model closures.
Physics of Plasmas,
Vol. 27,
Issue. 7,
Renganathan, S. Ashwin
Maulik, Romit
and
Rao, Vishwas
2020.
Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil.
Physics of Fluids,
Vol. 32,
Issue. 4,
Gin, Craig R.
Shea, Daniel E.
Brunton, Steven L.
and
Kutz, J. Nathan
2021.
DeepGreen: deep learning of Green’s functions for nonlinear boundary value problems.
Scientific Reports,
Vol. 11,
Issue. 1,
BURGER, M.
E, W.
RUTHOTTO, L.
and
OSHER, S. J.
2021.
Connections between deep learning and partial differential equations.
European Journal of Applied Mathematics,
Vol. 32,
Issue. 3,
p.
395.
Heaney, Claire E.
Wolffs, Zef
Tómasson, Jón Atli
Kahouadji, Lyes
Salinas, Pablo
Nicolle, André
Navon, Ionel M.
Matar, Omar K.
Srinil, Narakorn
and
Pain, Christopher C.
2022.
An AI-based non-intrusive reduced-order model for extended domains applied to multiphase flow in pipes.
Physics of Fluids,
Vol. 34,
Issue. 5,
Fieseler, C.
Mitchell, C. A.
Pyrak‐Nolte, L. J.
and
Kutz, J. N.
2022.
Characterization of Acoustic Emissions From Analogue Rocks Using Sparse Regression‐DMDc.
Journal of Geophysical Research: Solid Earth,
Vol. 127,
Issue. 7,
Baumann, Henry
Schaum, Alexander
and
Meurer, Thomas
2022.
Data-driven control-oriented reduced order modeling for open channel flows.
IFAC-PapersOnLine,
Vol. 55,
Issue. 26,
p.
193.
Brunton, Steven L.
Budišić, Marko
Kaiser, Eurika
and
Kutz, J. Nathan
2022.
Modern Koopman Theory for Dynamical Systems.
SIAM Review,
Vol. 64,
Issue. 2,
p.
229.
Bahmani, Bahador
and
Sun, WaiChing
2022.
Manifold embedding data-driven mechanics.
Journal of the Mechanics and Physics of Solids,
Vol. 166,
Issue. ,
p.
104927.
Kalu, Ikechukwu
Ndehedehe, Christopher E.
Okwuashi, Onuwa
Eyoh, Aniekan E.
and
Ferreira, Vagner G.
2022.
Geodetic first order data assimilation using an extended Kalman filtering technique.
Earth Science Informatics,
Vol. 15,
Issue. 4,
p.
2585.
Laperre, B.
Amaya, J.
Jamal, S.
and
Lapenta, G.
2022.
Identification of high order closure terms from fully kinetic simulations using machine learning.
Physics of Plasmas,
Vol. 29,
Issue. 3,
Kutz, J. Nathan
and
Brunton, Steven L.
2022.
Parsimony as the ultimate regularizer for physics-informed machine learning.
Nonlinear Dynamics,
Vol. 107,
Issue. 3,
p.
1801.
Kalu, Ikechukwu
Ndehedehe, Christopher E.
Okwuashi, Onuwa
and
Eyoh, Aniekan E.
2022.
A comparison of existing transformation models to improve coordinate conversion between geodetic reference frames in Nigeria.
Modeling Earth Systems and Environment,
Vol. 8,
Issue. 1,
p.
611.
Lim, Joowon
and
Psaltis, Demetri
2022.
MaxwellNet: Physics-driven deep neural network training based on Maxwell’s equations.
APL Photonics,
Vol. 7,
Issue. 1,
He, Xingchen
Yan, Lianshan
Jiang, Lin
Yi, Anlin
Pu, Zhengyu
Yu, Youren
Chen, Hongwei
Pan, Wei
and
Luo, Bin
2023.
Fourier Neural Operator for Accurate Optical Fiber Modeling With Low Complexity.
Journal of Lightwave Technology,
Vol. 41,
Issue. 8,
p.
2301.
Sholokhov, Aleksei
Liu, Yuying
Mansour, Hassan
and
Nabi, Saleh
2023.
Physics-informed neural ODE (PINODE): embedding physics into models using collocation points.
Scientific Reports,
Vol. 13,
Issue. 1,
Kaltenbach, Sebastian
Perdikaris, Paris
and
Koutsourelakis, Phaedon-Stelios
2023.
Semi-supervised invertible neural operators for Bayesian inverse problems.
Computational Mechanics,
Vol. 72,
Issue. 3,
p.
451.
Nathan Kutz, J.
2023.
Machine Learning in Modeling and Simulation.
p.
149.
Kutz, J. Nathan
2023.
Model Order Reduction and Applications.
Vol. 2328,
Issue. ,
p.
201.
Freire, Pedro
Manuylovich, Egor
Prilepsky, Jaroslaw E.
and
Turitsyn, Sergei K.
2023.
Artificial neural networks for photonic applications—from algorithms to implementation: tutorial.
Advances in Optics and Photonics,
Vol. 15,
Issue. 3,
p.
739.