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Extended physics-informed extreme learning machine for linear elastic fracture mechanics
Computer Methods in Applied Mechanics and EngineeringzbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhu, Bokai +2 more
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Physica Scripta
Abstract This study proposes a physics-informed extreme learning machine (PIELM) framework for solving the higher-order partial differential equations arising in electromechanical coupling of transversely isotropic piezoelectric beams with flexoelectric effects. The proposed approach replaces the neural network in
Anqing Li +6 more
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Abstract This study proposes a physics-informed extreme learning machine (PIELM) framework for solving the higher-order partial differential equations arising in electromechanical coupling of transversely isotropic piezoelectric beams with flexoelectric effects. The proposed approach replaces the neural network in
Anqing Li +6 more
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Applying Physics-Informed Extreme Learning Machines to Solve the Euler-Bernoulli Beam Problem
Theoretical and Natural SciencePhysics-Informed Neural Networks (PINNs) have been recently utilised to solve forward and backward partial differential equation problems. In this paper, we explore an alternative approach by using Physics-Informed Extreme Learning Machines (PIELM) to address the Euler-Bernoulli beam problem.
Shi Pan, Haolin Li
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Single- vs. Multilayer Physics-Informed Extreme Learning Machines for Orbit Determination
Orbit Determination (OD) is commonly addressed with classical estimators such as Weighted Least Squares, which are statistically well founded but can be sensitive to poor initialization and may degrade when the initial state is weakly known. Physics-Informed Machine Learning offers an alternative by embedding orbital dynamics directly into the ...Fabian Dallinger +3 more
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Physics-informed extreme learning machines for efficient Poisson equation solutions
Die Verwendung kleinerer neuronaler Netze zur Lösung der Poisson-Gleichung ist aufgrund ihrer Geschwindigkeit und Effizienz eine gute Alternative zu klassischen Ansätzen. Traditionelle numerische Methoden, wie die Finite-Elemente-Methode (FEM) und die Finite-Differenzen-Methode (FDM), können unter hohen Rechenkosten, schlechter Konditionierung und ...openaire +1 more source
Information-extreme machine learning on-board recognition system of ground objects with the adaptation of the input mathematical description [PDF]
Computer Modeling and Intelligent Systems, 2020 Anatoliy Dovbysh +3 more
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Physics-Informed Extreme Learning Machine Lyapunov Functions
IEEE Control Systems LettersRuikun Zhou +3 more
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Deep Physics-Informed Extreme Learning Machines for Orbit Determination
2025 International Conference on Space Robotics (iSpaRo)Fabian Dallinger +3 more
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Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine
Diagnostics, 2021Harpreet Singh +2 more
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