Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
Peyron, Mathis
Fillion, Anthony
Gürol, Selime
Marchais, Victor
Gratton, Serge
Boudier, Pierre
and
Goret, Gael
2021.
Latent space data assimilation by using deep learning.
Quarterly Journal of the Royal Meteorological Society,
Vol. 147,
Issue. 740,
p.
3759.
Taghizadeh, Salar
Witherden, Freddie D.
Hassan, Yassin A.
and
Girimaji, Sharath S.
2021.
Turbulence closure modeling with data-driven techniques: Investigation of generalizable deep neural networks.
Physics of Fluids,
Vol. 33,
Issue. 11,
Belak, Christoph
Hager, Oliver
Reimers, Charlotte
Schnell, Lotte
and
Würschmidt, Maximilian
2021.
Convergence Rates for a Deep Learning Algorithm for Semilinear PDEs.
SSRN Electronic Journal ,
Khoo, Yuehaw
Lu, Jianfeng
and
Ying, Lexing
2021.
Efficient Construction of Tensor Ring Representations from Sampling.
Multiscale Modeling & Simulation,
Vol. 19,
Issue. 3,
p.
1261.
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.
Bai, Xiao-Dong
and
Zhang, Dongxiao
2021.
Learning ground states of spin-orbit-coupled Bose-Einstein condensates by a theory-guided neural network.
Physical Review A,
Vol. 104,
Issue. 6,
Segurola-Gil, Lander
Zola, Francesco
Echeberria-Barrio, Xabier
and
Orduna-Urrutia, Raul
2021.
Machine Learning and Principles and Practice of Knowledge Discovery in Databases.
Vol. 1525,
Issue. ,
p.
55.
Pommer, Christian
Sinapius, Michael
Brysch, Marco
and
Al Natsheh, Naser
2021.
A NEAT Based Two Stage Neural Network Approach to Generate a Control Algorithm for a Pultrusion System.
AI,
Vol. 2,
Issue. 3,
p.
355.
Xu, Huan
and
Andrea, Murari
2022.
Prediction of Students’ Performance Based on the Hybrid IDA‐SVR Model.
Complexity,
Vol. 2022,
Issue. 1,
Hu, Ziqing
Liu, Chun
Wang, Yiwei
and
Xu, Zhiliang
2022.
Energetic Variational Neural Network Discretizations to Gradient Flows.
SSRN Electronic Journal,
Lei, Zhen
Shi, Lei
and
Zeng, Chenyu
2022.
Solving Parametric Partial Differential Equations with Deep Rectified Quadratic Unit Neural Networks.
Journal of Scientific Computing,
Vol. 93,
Issue. 3,
E, Weinan
Han, Jiequn
and
Jentzen, Arnulf
2022.
Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning.
Nonlinearity,
Vol. 35,
Issue. 1,
p.
278.
Mattey, Revanth
and
Ghosh, Susanta
2022.
A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations.
Computer Methods in Applied Mechanics and Engineering,
Vol. 390,
Issue. ,
p.
114474.
Grohs, Philipp
Jentzen, Arnulf
and
Salimova, Diyora
2022.
Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms.
Partial Differential Equations and Applications,
Vol. 3,
Issue. 4,
Rafiq, Muhammad
Rafiq, Ghazala
and
Choi, Gyu Sang
2022.
DSFA-PINN: Deep Spectral Feature Aggregation Physics Informed Neural Network.
IEEE Access,
Vol. 10,
Issue. ,
p.
22247.
Wang, Ting
and
Knap, Jaroslaw
2022.
Stochastic Deep-Ritz for Parametric Uncertainty Quantification.
SSRN Electronic Journal ,
Lin, Bo
Li, Qianxiao
and
Ren, Weiqing
2022.
Computing the Invariant Distribution of Randomly Perturbed Dynamical Systems Using Deep Learning.
Journal of Scientific Computing,
Vol. 91,
Issue. 3,
Bai, Xiao-Dong
and
Zhang, Dongxiao
2022.
Search for rogue waves in Bose-Einstein condensates via a theory-guided neural network.
Physical Review E,
Vol. 106,
Issue. 2,
Gu, Yiqi
and
Ng, Michael K.
2022.
Deep Adaptive Basis Galerkin Method for High-Dimensional Evolution Equations With Oscillatory Solutions.
SIAM Journal on Scientific Computing,
Vol. 44,
Issue. 5,
p.
A3130.
Li, Ye
Pang, Yiwen
and
Shan, Bin
2022.
Physics-guided Data Augmentation for Learning the Solution Operator of Linear Differential Equations.
p.
543.