Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
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,
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.
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.
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.
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.
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,
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.
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 ,
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,
Hu, Ziqing
Liu, Chun
Wang, Yiwei
and
Xu, Zhiliang
2022.
Energetic Variational Neural Network Discretizations to Gradient Flows.
SSRN Electronic Journal ,
Umeorah, Nneka
and
Mba, Jules Clement
2022.
Approximation of single‐barrier options partial differential equations using feed‐forward neural network.
Applied Stochastic Models in Business and Industry,
Vol. 38,
Issue. 6,
p.
1079.
Chen, Yongsheng
Yan, Jue
and
Zhong, Xinghui
2022.
Cell-average based neural network method for third order and fifth order KdV type equations.
Frontiers in Applied Mathematics and Statistics,
Vol. 8,
Issue. ,
Adcock, Ben
Cardenas, Juan M.
and
Dexter, Nick
2022.
CAS4DL: Christoffel adaptive sampling for function approximation via deep learning.
Sampling Theory, Signal Processing, and Data Analysis,
Vol. 20,
Issue. 2,
Li, Ye
Pang, Yiwen
and
Shan, Bin
2022.
Physics-guided Data Augmentation for Learning the Solution Operator of Linear Differential Equations.
p.
543.
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.
Ghani, Sufyan
and
Kumari, Sunita
2022.
Risk, Reliability and Sustainable Remediation in the Field of Civil and Environmental Engineering.
p.
183.
Zhang, Kai
Zuo, Yuande
Zhao, Hanjun
Ma, Xiaopeng
Gu, Jianwei
Wang, Jian
Yang, Yongfei
Yao, Chuanjin
and
Yao, Jun
2022.
Fourier Neural Operator for Solving Subsurface Oil/Water Two-Phase Flow Partial Differential Equation.
SPE Journal,
Vol. 27,
Issue. 03,
p.
1815.
Xu, Huan
and
Andrea, Murari
2022.
Prediction of Students’ Performance Based on the Hybrid IDA-SVR Model.
Complexity,
Vol. 2022,
Issue. ,
p.
1.
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.