Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System | Paper Citation Record

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This paper cites Cambridge University Press, 3 edition, 2007.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Cambridge University Press, 3 edition, 2007

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Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System The finite volume method

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This paper cites SIAM, 2007.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System SIAM, 2007

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This paper cites Pearson Education India, 2007.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Pearson Education India, 2007

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This paper cites Numerical methods for solving partial differential equations in applied physics.Frontiers in Applied Physics and Mathematics, 1(1):79–96, 2024.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Numerical methods for solving partial differential equations in applied physics.Frontiers in Applied Physics and Mathematics, 1(1):79–96, 2024

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This paper cites Academic press, 2014.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Academic press, 2014

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This paper cites Physics- informed machine learning.Nature Reviews Physics, 3(6):422–440, 2021.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Physics- informed machine learning.Nature Reviews Physics, 3(6):422–440, 2021

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This paper cites Scientific machine learning through physics–informed neural networks: Where we are and what’s next.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Scientific machine learning through physics–informed neural networks: Where we are and what’s next

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This paper cites Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning.Acta Numerica, 33:633–713, 2024.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning.Acta Numerica, 33:633–713, 2024

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This paper cites Algorithms for solving high dimensional pdes: from nonlinear monte carlo to machine learning.Nonlinearity, 35(1):278, 2021.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Algorithms for solving high dimensional pdes: from nonlinear monte carlo to machine learning.Nonlinearity, 35(1):278, 2021

Reference 11

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This paper cites Robust control of uncertain quantum systems based on physics-informed neural networks and sampling learning.IEEE Transactions on Artificial Intelligence, 2025.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Robust control of uncertain quantum systems based on physics-informed neural networks and sampling learning.IEEE Transactions on Artificial Intelligence, 2025

Reference 12

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This paper cites Physics-informed neural networks for modeling water flows in a river channel.IEEE Transactions on Artificial Intelligence, 5(3):1001–1015, 2022.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Physics-informed neural networks for modeling water flows in a river channel.IEEE Transactions on Artificial Intelligence, 5(3):1001–1015, 2022

Reference 13

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This paper cites Quantasio: A generalized neural framework for solving 3d navier-stokes dynamics.IEEE Transactions on Artificial Intelligence, 2025.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Quantasio: A generalized neural framework for solving 3d navier-stokes dynamics.IEEE Transactions on Artificial Intelligence, 2025

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This paper cites Neural network methods based on efficient optimization algorithms for solving impulsive differential equations.IEEE Transactions on Artificial Intelligence, 5(3):1067– 1076, 2024.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Neural network methods based on efficient optimization algorithms for solving impulsive differential equations.IEEE Transactions on Artificial Intelligence, 5(3):1067– 1076, 2024

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This paper cites Adam: A Method for Stochastic Optimization.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Adam: A Method for Stochastic Optimization

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Observation 75749808-41c6-4519-a542-0946e9f913dc · outbound

This paper cites On the limited memory bfgs method for large scale optimization.Mathematical programming, 45(1):503–528, 1989.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System On the limited memory bfgs method for large scale optimization.Mathematical programming, 45(1):503–528, 1989

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This paper cites Theory of the backpropagation neural network.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Theory of the backpropagation neural network

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This paper cites The old and the new: Can physics-informed deep-learning replace traditional linear solvers? Frontiers in big Data, 4:669097, 2021.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System The old and the new: Can physics-informed deep-learning replace traditional linear solvers? Frontiers in big Data, 4:669097, 2021

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This paper cites A survey on statistical theory of deep learning: Approximation, training dynamics, and generative models.Annual Review of Statistics and Its Application, 12, 2024.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System A survey on statistical theory of deep learning: Approximation, training dynamics, and generative models.Annual Review of Statistics and Its Application, 12, 2024

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This paper cites Characterizing possible failure modes in physics-informed neural networks.Advances in neural information processing systems, 34:26548–26560, 2021.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Characterizing possible failure modes in physics-informed neural networks.Advances in neural information processing systems, 34:26548–26560, 2021

Reference 21

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This paper cites Physics informed extreme learning machine (pielm)–a rapid method for the numerical solution of partial differential equations.Neurocomputing, 391:96–118, 2020.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Physics informed extreme learning machine (pielm)–a rapid method for the numerical solution of partial differential equations.Neurocomputing, 391:96–118, 2020

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This paper cites JHU press, 2013.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System JHU press, 2013

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This paper cites A review of multilayer extreme learning machine neural networks.Artificial Intelligence Review, 56(11):13691–13742, 2023.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System A review of multilayer extreme learning machine neural networks.Artificial Intelligence Review, 56(11):13691–13742, 2023

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Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Unresolved cited work

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This paper cites Learning and generalization characteristics of the random vector functional-link net.Neurocomputing, 6(2):163–180, 1994.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Learning and generalization characteristics of the random vector functional-link net.Neurocomputing, 6(2):163–180, 1994

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This paper cites Stochastic choice of basis functions in adaptive function approximation and the functional-link net.IEEE transactions on Neural Networks, 6(6):1320–1329, 1995.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Stochastic choice of basis functions in adaptive function approximation and the functional-link net.IEEE transactions on Neural Networks, 6(6):1320–1329, 1995

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This paper cites Springer, 2003.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Springer, 2003

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This paper cites Universal approximation capability of broad learning system and its structural variations.IEEE transactions on neural networks and learning systems, 30(4):1191–1204, 2018.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Universal approximation capability of broad learning system and its structural variations.IEEE transactions on neural networks and learning systems, 30(4):1191–1204, 2018

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Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Unresolved cited work

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This paper cites Research review for broad learning system: Algorithms, theory, and applications.IEEE Transactions on Cybernetics, 52(9):8922–8950, 2021.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Research review for broad learning system: Algorithms, theory, and applications.IEEE Transactions on Cybernetics, 52(9):8922–8950, 2021

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This paper cites Analysis and variants of broad learning system.IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(1):334–344, 2020.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Analysis and variants of broad learning system.IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(1):334–344, 2020

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This paper cites Philip Chen.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Philip Chen

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This paper cites Broad convolutional neural network based industrial process fault diagnosis with incremental learning capability.IEEE Transactions on Industrial Electronics, 67(6):5081–5091, 2020.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Broad convolutional neural network based industrial process fault diagnosis with incremental learning capability.IEEE Transactions on Industrial Electronics, 67(6):5081–5091, 2020

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Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Unresolved cited work

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Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Unresolved cited work

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Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Unresolved cited work

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This paper cites PINN for Dynamical Partial Differential Equations is Not Training Deeper Networks Rather Learning Advection and Time Variance.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System PINN for Dynamical Partial Differential Equations is Not Training Deeper Networks Rather Learning Advection and Time Variance

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Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System On the training efficiency of shallow architectures for physics informed neural networks

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This paper cites SIAM, 1995.

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System SIAM, 1995

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Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System An interior trust region approach for nonlinear minimization subject to bounds

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Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Unresolved cited work

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Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Elsevier, 2003

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Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Approximation Theory and Applications of Randomized Neural Networks for Solving High-Dimensional PDEs

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Observation dca0de81-5aa9-4ffa-83c5-36da0bd7ae3e · outbound

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Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System A unified deep artificial neural network approach to partial differential equations in complex geometries.Neurocomputing, 317:28–41, 2018

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Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System Artificial neural networks for solving ordinary and partial differential equations.IEEE transactions on neural networks, 9(5):987–1000, 1998

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