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Yeonjong Shin
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2020 – today
- 2024
- [j14]Sanghyun Lee, Yeonjong Shin:
On the Training and Generalization of Deep Operator Networks. SIAM J. Sci. Comput. 46(4): 273- (2024) - [j13]Jessica T. Lauzon, Siu Wun Cheung, Yeonjong Shin, Youngsoo Choi, Dylan Matthew Copeland, Kevin Huynh:
S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models. SIAM J. Sci. Comput. 46(4): 474- (2024) - [i15]Jun Sur Richard Park, Siu Wun Cheung, Youngsoo Choi, Yeonjong Shin:
tLaSDI: Thermodynamics-informed latent space dynamics identification. CoRR abs/2403.05848 (2024) - [i14]Christophe Bonneville, Xiaolong He, April Tran, Jun Sur Richard Park, William Fries, Daniel A. Messenger, Siu Wun Cheung, Yeonjong Shin, David M. Bortz, Debojyoti Ghosh, Jiun-Shyan Chen, Jonathan L. Belof, Youngsoo Choi:
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling. CoRR abs/2403.10748 (2024) - 2023
- [j12]Yeonjong Shin, Jérôme Darbon, George Em Karniadakis:
Accelerating gradient descent and Adam via fractional gradients. Neural Networks 161: 185-201 (2023) - [i13]Sanghyun Lee, Yeonjong Shin:
On the training and generalization of deep operator networks. CoRR abs/2309.01020 (2023) - [i12]Khemraj Shukla, Yeonjong Shin:
Randomized Forward Mode of Automatic Differentiation for Optimization Algorithms. CoRR abs/2310.14168 (2023) - 2022
- [j11]Ameya D. Jagtap, Yeonjong Shin, Kenji Kawaguchi, George Em Karniadakis:
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions. Neurocomputing 468: 165-180 (2022) - [j10]Beichuan Deng, Yeonjong Shin, Lu Lu, Zhongqiang Zhang, George Em Karniadakis:
Approximation rates of DeepONets for learning operators arising from advection-diffusion equations. Neural Networks 153: 411-426 (2022) - [j9]Mark Ainsworth, Yeonjong Shin:
Active Neuron Least Squares: A Training Method for Multivariate Rectified Neural Networks. SIAM J. Sci. Comput. 44(4): 2253- (2022) - [i11]Jessica T. Lauzon, Siu Wun Cheung, Yeonjong Shin, Youngsoo Choi, Dylan Matthew Copeland, Kevin Huynh:
S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models. CoRR abs/2203.16494 (2022) - 2021
- [j8]Mark Ainsworth, Yeonjong Shin:
Plateau Phenomenon in Gradient Descent Training of RELU Networks: Explanation, Quantification, and Avoidance. SIAM J. Sci. Comput. 43(5): A3438-A3468 (2021) - [i10]Beichuan Deng, Yeonjong Shin, Lu Lu, Zhongqiang Zhang, George Em Karniadakis:
Convergence rate of DeepONets for learning operators arising from advection-diffusion equations. CoRR abs/2102.10621 (2021) - [i9]Yeonjong Shin, Jérôme Darbon, George Em Karniadakis:
A Caputo fractional derivative-based algorithm for optimization. CoRR abs/2104.02259 (2021) - [i8]Ameya D. Jagtap, Yeonjong Shin, Kenji Kawaguchi, George Em Karniadakis:
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions. CoRR abs/2105.09513 (2021) - [i7]Zhen Zhang, Yeonjong Shin, George Em Karniadakis:
GFINNs: GENERIC Formalism Informed Neural Networks for Deterministic and Stochastic Dynamical Systems. CoRR abs/2109.00092 (2021) - 2020
- [i6]Yeonjong Shin, Jérôme Darbon, George Em Karniadakis:
On the Convergence and generalization of Physics Informed Neural Networks. CoRR abs/2004.01806 (2020) - [i5]Mark Ainsworth, Yeonjong Shin:
Plateau Phenomenon in Gradient Descent Training of ReLU networks: Explanation, Quantification and Avoidance. CoRR abs/2007.07213 (2020) - [i4]Yeonjong Shin, Zhongqiang Zhang, George Em Karniadakis:
Error estimates of residual minimization using neural networks for linear PDEs. CoRR abs/2010.08019 (2020)
2010 – 2019
- 2019
- [i3]Lu Lu, Yeonjong Shin, Yanhui Su, George E. Karniadakis:
Dying ReLU and Initialization: Theory and Numerical Examples. CoRR abs/1903.06733 (2019) - [i2]Yeonjong Shin, George E. Karniadakis:
Trainability and Data-dependent Initialization of Over-parameterized ReLU Neural Networks. CoRR abs/1907.09696 (2019) - [i1]Yeonjong Shin:
Effects of Depth, Width, and Initialization: A Convergence Analysis of Layer-wise Training for Deep Linear Neural Networks. CoRR abs/1910.05874 (2019) - 2018
- [j7]Yeonjong Shin, Kailiang Wu, Dongbin Xiu:
Sequential function approximation with noisy data. J. Comput. Phys. 371: 363-381 (2018) - 2017
- [j6]Liang Yan, Yeonjong Shin, Dongbin Xiu:
Sparse Approximation using ℓ1-ℓ2 Minimization and Its Application to Stochastic Collocation. SIAM J. Sci. Comput. 39(1) (2017) - [j5]Yeonjong Shin, Dongbin Xiu:
A Randomized Algorithm for Multivariate Function Approximation. SIAM J. Sci. Comput. 39(3) (2017) - [j4]Kailiang Wu, Yeonjong Shin, Dongbin Xiu:
A Randomized Tensor Quadrature Method for High Dimensional Polynomial Approximation. SIAM J. Sci. Comput. 39(5) (2017) - 2016
- [j3]Yeonjong Shin, Dongbin Xiu:
On a near optimal sampling strategy for least squares polynomial regression. J. Comput. Phys. 326: 931-946 (2016) - [j2]Yeonjong Shin, Dongbin Xiu:
Nonadaptive Quasi-Optimal Points Selection for Least Squares Linear Regression. SIAM J. Sci. Comput. 38(1) (2016) - [j1]Yeonjong Shin, Dongbin Xiu:
Correcting Data Corruption Errors for Multivariate Function Approximation. SIAM J. Sci. Comput. 38(4) (2016)
Coauthor Index
aka: George E. Karniadakis
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last updated on 2024-08-23 18:35 CEST by the dblp team
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