Yeonjong Shin - RankMe

33 papers Journal 33

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems. Trans. Mach. Learn. Res. Xiaolong He, Yeonjong Shin, Anthony Gruber, Sohyeon Jung, Kookjin Lee, Youngsoo Choi
2025	J	`jnl`	Deep operator network for surrogate modeling of poroelasticity with random permeability fields. CoRR Sangjoon Park, Yeonjong Shin, Jinhyun Choo
2025	J	`jnl`	Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators. CoRR Weihang Ouyang, Yeonjong Shin, Si-Wei Liu, Lu Lu
2025	J	`jnl`	Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems. CoRR Xiaolong He, Yeonjong Shin, Anthony Gruber, Sohyeon Jung, Kookjin Lee, Youngsoo Choi
2024	J	`jnl`	A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling. CoRR 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
2024	J	`jnl`	On the Training and Generalization of Deep Operator Networks. SIAM J. Sci. Comput. Sanghyun Lee, Yeonjong Shin
2024	J	`jnl`	S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models. SIAM J. Sci. Comput. Jessica T. Lauzon, Siu Wun Cheung, Yeonjong Shin, Youngsoo Choi, Dylan Matthew Copeland, Kevin Huynh
2024	J	`jnl`	tLaSDI: Thermodynamics-informed latent space dynamics identification. CoRR Jun Sur Richard Park, Siu Wun Cheung, Youngsoo Choi, Yeonjong Shin
2023	J	`jnl`	Accelerating gradient descent and Adam via fractional gradients. Neural Networks Yeonjong Shin, Jérôme Darbon, George Em Karniadakis
2023	J	`jnl`	On the training and generalization of deep operator networks. CoRR Sanghyun Lee, Yeonjong Shin
2023	J	`jnl`	Randomized Forward Mode of Automatic Differentiation for Optimization Algorithms. CoRR Khemraj Shukla, Yeonjong Shin
2022	J	`jnl`	Active Neuron Least Squares: A Training Method for Multivariate Rectified Neural Networks. SIAM J. Sci. Comput. Mark Ainsworth, Yeonjong Shin
2022	J	`jnl`	Approximation rates of DeepONets for learning operators arising from advection-diffusion equations. Neural Networks Beichuan Deng, Yeonjong Shin, Lu Lu, Zhongqiang Zhang, George Em Karniadakis
2022	J	`jnl`	Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions. Neurocomputing Ameya D. Jagtap, Yeonjong Shin, Kenji Kawaguchi, George Em Karniadakis
2022	J	`jnl`	S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models. CoRR Jessica T. Lauzon, Siu Wun Cheung, Yeonjong Shin, Youngsoo Choi, Dylan Matthew Copeland, Kevin Huynh
2021	J	`jnl`	A Caputo fractional derivative-based algorithm for optimization. CoRR Yeonjong Shin, Jérôme Darbon, George Em Karniadakis
2021	J	`jnl`	Convergence rate of DeepONets for learning operators arising from advection-diffusion equations. CoRR Beichuan Deng, Yeonjong Shin, Lu Lu, Zhongqiang Zhang, George Em Karniadakis
2021	J	`jnl`	Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions. CoRR Ameya D. Jagtap, Yeonjong Shin, Kenji Kawaguchi, George Em Karniadakis
2021	J	`jnl`	GFINNs: GENERIC Formalism Informed Neural Networks for Deterministic and Stochastic Dynamical Systems. CoRR Zhen Zhang, Yeonjong Shin, George Em Karniadakis
2021	J	`jnl`	Plateau Phenomenon in Gradient Descent Training of RELU Networks: Explanation, Quantification, and Avoidance. SIAM J. Sci. Comput. Mark Ainsworth, Yeonjong Shin
2020	J	`jnl`	Error estimates of residual minimization using neural networks for linear PDEs. CoRR Yeonjong Shin, Zhongqiang Zhang, George Em Karniadakis
2020	J	`jnl`	On the Convergence and generalization of Physics Informed Neural Networks. CoRR Yeonjong Shin, Jérôme Darbon, George Em Karniadakis
2020	J	`jnl`	Plateau Phenomenon in Gradient Descent Training of ReLU networks: Explanation, Quantification and Avoidance. CoRR Mark Ainsworth, Yeonjong Shin
2019	J	`jnl`	Dying ReLU and Initialization: Theory and Numerical Examples. CoRR Lu Lu, Yeonjong Shin, Yanhui Su, George E. Karniadakis
2019	J	`jnl`	Effects of Depth, Width, and Initialization: A Convergence Analysis of Layer-wise Training for Deep Linear Neural Networks. CoRR Yeonjong Shin
2019	J	`jnl`	Trainability and Data-dependent Initialization of Over-parameterized ReLU Neural Networks. CoRR Yeonjong Shin, George E. Karniadakis
2018	J	`jnl`	Sequential function approximation with noisy data. J. Comput. Phys. Yeonjong Shin, Kailiang Wu, Dongbin Xiu
2017	J	`jnl`	A Randomized Algorithm for Multivariate Function Approximation. SIAM J. Sci. Comput. Yeonjong Shin, Dongbin Xiu
2017	J	`jnl`	A Randomized Tensor Quadrature Method for High Dimensional Polynomial Approximation. SIAM J. Sci. Comput. Kailiang Wu, Yeonjong Shin, Dongbin Xiu
2017	J	`jnl`	Sparse Approximation using ℓ SIAM J. Sci. Comput. Liang Yan, Yeonjong Shin, Dongbin Xiu
2016	J	`jnl`	Correcting Data Corruption Errors for Multivariate Function Approximation. SIAM J. Sci. Comput. Yeonjong Shin, Dongbin Xiu
2016	J	`jnl`	Nonadaptive Quasi-Optimal Points Selection for Least Squares Linear Regression. SIAM J. Sci. Comput. Yeonjong Shin, Dongbin Xiu
2016	J	`jnl`	On a near optimal sampling strategy for least squares polynomial regression. J. Comput. Phys. Yeonjong Shin, Dongbin Xiu