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A Modified Batch Intrinsic Plasticity Method for Pre-training the Random
  Coefficients of Extreme Learning Machines

A Modified Batch Intrinsic Plasticity Method for Pre-training the Random Coefficients of Extreme Learning Machines

Journal of Computational Physics (JCP), 2021
14 March 2021
S. Dong
Zongwei Li
ArXiv (abs)PDFHTML

Papers citing "A Modified Batch Intrinsic Plasticity Method for Pre-training the Random Coefficients of Extreme Learning Machines"

10 / 10 papers shown
HEATNETs: Explainable Random Feature Neural Networks for High-Dimensional Parabolic PDEs
HEATNETs: Explainable Random Feature Neural Networks for High-Dimensional Parabolic PDEs
Kyriakos Georgiou
Gianluca Fabiani
Constantinos Siettos
A. Yannacopoulos
150
0
0
02 Nov 2025
Weak baselines and reporting biases lead to overoptimism in machine
  learning for fluid-related partial differential equations
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
N. McGreivy
Ammar Hakim
AI4CE
303
127
0
09 Jul 2024
An Extreme Learning Machine-Based Method for Computational PDEs in
  Higher Dimensions
An Extreme Learning Machine-Based Method for Computational PDEs in Higher DimensionsComputer Methods in Applied Mechanics and Engineering (CMAME), 2023
Yiran Wang
Suchuan Dong
368
53
0
13 Sep 2023
Error Analysis of Physics-Informed Neural Networks for Approximating
  Dynamic PDEs of Second Order in Time
Error Analysis of Physics-Informed Neural Networks for Approximating Dynamic PDEs of Second Order in Time
Y. Qian
Yongchao Zhang
Yuanfei Huang
S. Dong
PINN
246
2
0
22 Mar 2023
A Method for Computing Inverse Parametric PDE Problems with
  Random-Weight Neural Networks
A Method for Computing Inverse Parametric PDE Problems with Random-Weight Neural NetworksJournal of Computational Physics (JCP), 2022
S. Dong
Yiran Wang
240
32
0
09 Oct 2022
Numerical Computation of Partial Differential Equations by Hidden-Layer
  Concatenated Extreme Learning Machine
Numerical Computation of Partial Differential Equations by Hidden-Layer Concatenated Extreme Learning MachineJournal of Scientific Computing (J. Sci. Comput.), 2022
Naxian Ni
S. Dong
326
27
0
24 Apr 2022
Parsimonious Physics-Informed Random Projection Neural Networks for
  Initial-Value Problems of ODEs and index-1 DAEs
Parsimonious Physics-Informed Random Projection Neural Networks for Initial-Value Problems of ODEs and index-1 DAEsChaos (Chaos), 2022
Gianluca Fabiani
Evangelos Galaris
L. Russo
Constantinos Siettos
176
38
0
10 Mar 2022
Numerical Approximation of Partial Differential Equations by a Variable
  Projection Method with Artificial Neural Networks
Numerical Approximation of Partial Differential Equations by a Variable Projection Method with Artificial Neural NetworksComputer Methods in Applied Mechanics and Engineering (CMAME), 2022
S. Dong
Jielin Yang
239
24
0
24 Jan 2022
On Computing the Hyperparameter of Extreme Learning Machines: Algorithm
  and Application to Computational PDEs, and Comparison with Classical and
  High-Order Finite Elements
On Computing the Hyperparameter of Extreme Learning Machines: Algorithm and Application to Computational PDEs, and Comparison with Classical and High-Order Finite Elements
S. Dong
Jielin Yang
261
68
0
27 Oct 2021
Numerical Solution of Stiff ODEs with Physics-Informed RPNNs
Numerical Solution of Stiff ODEs with Physics-Informed RPNNs
Evangelos Galaris
Gianluca Fabiani
Francesco Calabrò
D. Serafino
Constantinos Siettos
260
3
0
03 Aug 2021
1
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