On a near optimal sampling strategy for least squares polynomial regression. - Yeonjong Shin; Dongbin Xiu (J. Comput. Phys., 2016)

Nonadaptive Quasi-Optimal Points Selection for Least Squares Linear Regression. - Yeonjong Shin; Dongbin Xiu (SIAM J. Sci. Comput., 2016)

Correcting Data Corruption Errors for Multivariate Function Approximation. - Yeonjong Shin; Dongbin Xiu (SIAM J. Sci. Comput., 2016)

A Randomized Algorithm for Multivariate Function Approximation. - Yeonjong Shin; Dongbin Xiu (SIAM J. Sci. Comput., 2017)

A Randomized Tensor Quadrature Method for High Dimensional Polynomial Approximation. - Kailiang Wu; Yeonjong Shin; Dongbin Xiu (SIAM J. Sci. Comput., 2017)

Sparse Approximation using ℓ1-ℓ2 Minimization and Its Application to Stochastic Collocation. - Liang Yan 002; Yeonjong Shin; Dongbin Xiu (SIAM J. Sci. Comput., 2017)

Sequential function approximation with noisy data. - Yeonjong Shin; Kailiang Wu; Dongbin Xiu (J. Comput. Phys., 2018)

Dying ReLU and Initialization: Theory and Numerical Examples. - Lu Lu 010; Yeonjong Shin; Yanhui Su; George E. Karniadakis (CoRR, 2019)

Trainability and Data-dependent Initialization of Over-parameterized ReLU Neural Networks. - Yeonjong Shin; George E. Karniadakis (CoRR, 2019)

Effects of Depth, Width, and Initialization: A Convergence Analysis of Layer-wise Training for Deep Linear Neural Networks. - Yeonjong Shin (CoRR, 2019)

On the Convergence and generalization of Physics Informed Neural Networks. - Yeonjong Shin; Jérôme Darbon; George Em Karniadakis (CoRR, 2020)

Error estimates of residual minimization using neural networks for linear PDEs. - Yeonjong Shin; Zhongqiang Zhang; George Em Karniadakis (CoRR, 2020)

Convergence rate of DeepONets for learning operators arising from advection-diffusion equations. - Beichuan Deng; Yeonjong Shin; Lu Lu 010; Zhongqiang Zhang; George Em Karniadakis (CoRR, 2021)

A Caputo fractional derivative-based algorithm for optimization. - Yeonjong Shin; Jérôme Darbon; George Em Karniadakis (CoRR, 2021)

GFINNs: GENERIC Formalism Informed Neural Networks for Deterministic and Stochastic Dynamical Systems. - Zhen Zhang 029; Yeonjong Shin; George Em Karniadakis (CoRR, 2021)

Plateau Phenomenon in Gradient Descent Training of RELU Networks: Explanation, Quantification, and Avoidance. - Mark Ainsworth; Yeonjong Shin (SIAM J. Sci. Comput., 2021)

Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions. - Ameya D. Jagtap; Yeonjong Shin; Kenji Kawaguchi; George Em Karniadakis (Neurocomputing, 2022)

Approximation rates of DeepONets for learning operators arising from advection-diffusion equations. - Beichuan Deng; Yeonjong Shin; Lu Lu 010; Zhongqiang Zhang; George Em Karniadakis (Neural Networks, 2022)

Active Neuron Least Squares: A Training Method for Multivariate Rectified Neural Networks. - Mark Ainsworth; Yeonjong Shin (SIAM J. Sci. Comput., 2022)

Randomized Forward Mode of Automatic Differentiation for Optimization Algorithms. - Khemraj Shukla; Yeonjong Shin (CoRR, 2023)

Accelerating gradient descent and Adam via fractional gradients. - Yeonjong Shin; Jérôme Darbon; George Em Karniadakis (Neural Networks, 2023)

tLaSDI: Thermodynamics-informed latent space dynamics identification. - Jun Sur Richard Park; Siu Wun Cheung; Youngsoo Choi; Yeonjong Shin (CoRR, 2024)

A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling. - Christophe Bonneville; Xiaolong He 001; 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; et al. (CoRR, 2024)

S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models. - Jessica T. Lauzon; Siu Wun Cheung; Yeonjong Shin; Youngsoo Choi; Dylan Matthew Copeland; Kevin Huynh (SIAM J. Sci. Comput., 2024)

On the Training and Generalization of Deep Operator Networks. - Sanghyun Lee; Yeonjong Shin (SIAM J. Sci. Comput., 2024)

Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems. - Xiaolong He 001; Yeonjong Shin; Anthony Gruber; Sohyeon Jung; Kookjin Lee; Youngsoo Choi (CoRR, 2025)

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators. - Weihang Ouyang; Yeonjong Shin; Si-Wei Liu; Lu Lu (CoRR, 2025)