Yeonjong Shin

SIAM J. Sci. Comput., 2024

A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling.

[BibT_eX]

[DOI]

Christophe Bonneville

CoRR, 2024

tLaSDI: Thermodynamics-informed latent space dynamics identification.

[BibT_eX]

[DOI]

CoRR, 2024

2023

Accelerating gradient descent and Adam via fractional gradients.

[BibT_eX]

[DOI]

Jérôme Darbon

Neural Networks, April, 2023

Randomized Forward Mode of Automatic Differentiation for Optimization Algorithms.

[BibT_eX]

[DOI]

Khemraj Shukla

CoRR, 2023

2022

Active Neuron Least Squares: A Training Method for Multivariate Rectified Neural Networks.

[BibT_eX]

[DOI]

Mark Ainsworth

SIAM J. Sci. Comput., August, 2022

Approximation rates of DeepONets for learning operators arising from advection-diffusion equations.

[BibT_eX]

[DOI]

Neural Networks, 2022

Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions.

[BibT_eX]

[DOI]

Ameya D. Jagtap

Kenji Kawaguchi

Neurocomputing, 2022

S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models.

[BibT_eX]

[DOI]

Dylan Matthew Copeland

Kevin Huynh

CoRR, 2022

2021

Plateau Phenomenon in Gradient Descent Training of RELU Networks: Explanation, Quantification, and Avoidance.

[BibT_eX]

[DOI]

Mark Ainsworth

SIAM J. Sci. Comput., 2021

GFINNs: GENERIC Formalism Informed Neural Networks for Deterministic and Stochastic Dynamical Systems.

[BibT_eX]

[DOI]

Zhen Zhang

CoRR, 2021

A Caputo fractional derivative-based algorithm for optimization.

[BibT_eX]

[DOI]

Jérôme Darbon

CoRR, 2021

Convergence rate of DeepONets for learning operators arising from advection-diffusion equations.

[BibT_eX]

[DOI]

CoRR, 2021

2020

Error estimates of residual minimization using neural networks for linear PDEs.

[BibT_eX]

[DOI]

Zhongqiang Zhang

CoRR, 2020

On the Convergence and generalization of Physics Informed Neural Networks.

[BibT_eX]

[DOI]

Jérôme Darbon

CoRR, 2020

2019

Effects of Depth, Width, and Initialization: A Convergence Analysis of Layer-wise Training for Deep Linear Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2019

Trainability and Data-dependent Initialization of Over-parameterized ReLU Neural Networks.

[BibT_eX]

[DOI]

George E. Karniadakis

CoRR, 2019

Dying ReLU and Initialization: Theory and Numerical Examples.

[BibT_eX]

[DOI]

Lu Lu

Yanhui Su

George E. Karniadakis

CoRR, 2019

2018

Sequential function approximation with noisy data.

[BibT_eX]

[DOI]

Kailiang Wu

J. Comput. Phys., 2018

2017

Sparse Approximation using ℓ<sub>1-ℓ<sub>2</sub></sub> Minimization and Its Application to Stochastic Collocation.

[BibT_eX]

[DOI]

Liang Yan

SIAM J. Sci. Comput., 2017

A Randomized Tensor Quadrature Method for High Dimensional Polynomial Approximation.

[BibT_eX]

[DOI]

Kailiang Wu

SIAM J. Sci. Comput., 2017

A Randomized Algorithm for Multivariate Function Approximation.

[BibT_eX]

[DOI]

SIAM J. Sci. Comput., 2017

2016

Correcting Data Corruption Errors for Multivariate Function Approximation.

[BibT_eX]

[DOI]

SIAM J. Sci. Comput., 2016

Nonadaptive Quasi-Optimal Points Selection for Least Squares Linear Regression.

[BibT_eX]

[DOI]

SIAM J. Sci. Comput., 2016

On a near optimal sampling strategy for least squares polynomial regression.

[BibT_eX]

[DOI]