On the Training and Generalization of Deep Operator Networks von Lee, Sanghyun, Shin, Yeonjong

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Plateau Phenomenon in Gradient Descent Training of RELU Networks: Explanation, Quantification, and Avoidance von Ainsworth, Mark, Shin, Yeonjong

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Active Neuron Least Squares: A Training Method for Multivariate Rectified Neural Networks von Ainsworth, Mark, Shin, Yeonjong

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Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions von Jagtap, Ameya D., Shin, Yeonjong, Kawaguchi, Kenji, Karniadakis, George Em

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On a near optimal sampling strategy for least squares polynomial regression von Shin, Yeonjong, Xiu, Dongbin

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Nonadaptive Quasi-Optimal Points Selection for Least Squares Linear Regression von Shin, Yeonjong, Xiu, Dongbin

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GFINNs: GENERIC formalism informed neural networks for deterministic and stochastic dynamical systems von Zhang, Zhen, Shin, Yeonjong, Em Karniadakis, George

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A Randomized Algorithm for Multivariate Function Approximation von Shin, Yeonjong, Xiu, Dongbin

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Approximation rates of DeepONets for learning operators arising from advection–diffusion equations von Deng, Beichuan, Shin, Yeonjong, Lu, Lu, Zhang, Zhongqiang, Karniadakis, George Em

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Correcting Data Corruption Errors for Multivariate Function Approximation von Shin, Yeonjong, Xiu, Dongbin

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Accelerating gradient descent and Adam via fractional gradients von Shin, Yeonjong, Darbon, Jérôme, Karniadakis, George Em

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Sequential function approximation with noisy data von Shin, Yeonjong, Wu, Kailiang, Xiu, Dongbin

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tLaSDI: Thermodynamics-informed latent space dynamics identification von Park, Jun Sur Richard, Cheung, Siu Wun, Choi, Youngsoo, Shin, Yeonjong

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S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models von Lauzon, Jessica T., Cheung, Siu Wun, Shin, Yeonjong, Choi, Youngsoo, Copeland, Dylan M., Huynh, Kevin

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Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions von Jagtap, Ameya D., Shin, Yeonjong, Kawaguchi, Kenji, Karniadakis, George Em

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Approximation rates of DeepONets for learning operators arising from advection–diffusion equations von Deng, Beichuan, Shin, Yeonjong, Lu, Lu, Zhang, Zhongqiang, Karniadakis, George Em

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Sparse Approximation using $\ell_1-\ell_2$ Minimization and Its Application to Stochastic Collocation von Yan, Liang, Shin, Yeonjong, Xiu, Dongbin

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A Randomized Tensor Quadrature Method for High Dimensional Polynomial Approximation von Wu, Kailiang, Shin, Yeonjong, Xiu, Dongbin

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Randomized Forward Mode of Automatic Differentiation For Optimization Algorithms von Shukla, Khemraj, Shin, Yeonjong

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On the training and generalization of deep operator networks von Lee, Sanghyun, Shin, Yeonjong

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