Physics-informed neural networks with hybrid Kolmogorov-Arnold network and augmented Lagrangian function for solving partial differential equations [PDF]
Scientific ReportsPhysics-informed neural networks (PINNs) have emerged as a fundamental approach within deep learning for the resolution of partial differential equations (PDEs).
Zhaoyang Zhang +4 more
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Multiple general sigmoids based Banach space valued neural network multivariate approximation
Cubo, 2023 Here we present multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or \(\mathbb{R}^{N},\) \(N\in \mathbb{N}\), by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature ...
George A. Anastassiou
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Comparison of bayesian regularized neural network, random forest regression, support vector regression and multivariate adaptive regression splines algorithms to predict body weight from biometrical measurements in thalli sheep [PDF]
Kafkas Universitesi Veteriner Fakültesi Dergisi, 2022 In this study, it is aimed to compare several data mining and artificial neural network algorithms to predict body weight from biometric measurements for the Th alli sheep breed.
Cem TIRINK
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General multivariate arctangent function activated neural network approximations
Journal of Numerical Analysis and Approximation Theory, 2022 Here we expose multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or \(\mathbb{R}^{N}\), \(N\in \mathbb{N}\), by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature ...
George A. Anastassiou
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Neural Network Approximation for Time Splitting Random Functions
Mathematics, 2023 In this article we present the multivariate approximation of time splitting random functions defined on a box or RN,N∈N, by neural network operators of quasi-interpolation type.
George A. Anastassiou +1 more
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Hyperbolic Tangent Like Relied Banach Space Valued Neural Network Multivariate Approximations
Annals of the West University of Timisoara: Mathematics and Computer Science, 2023 Here we examine the multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or ℝN , N ∈ ℕ, by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network ...
Anastassiou George A.
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IEEE Access, 2023 Locomotive traction energy consumption is a multivariate coupled nonlinear system closely related to many factors such as locomotive properties, routing, line conditions, and operating methods.
Huize Liang +4 more
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Journal of Control Science and Engineering, 2022 In order to solve the problem that the traditional long-term high-speed traffic forecasting algorithm is affected by the approximation ability of the function and easy to fall into the local mass value, we wrote a multivariate-based highway traffic ...
Yuzhu Luo, Jiarong Wang, Ming Wei
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Improved Uncertainty Quantification for Neural Networks With Bayesian Last Layer
IEEE Access, 2023 Uncertainty quantification is an important task in machine learning - a task in which standard neural networks (NNs) have traditionally not excelled. This can be a limitation for safety-critical applications, where uncertainty-aware methods like Gaussian
Felix Fiedler, Sergio Lucia
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A machine learning approach to portfolio pricing and risk management for high-dimensional problems
, 2021 We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting.
Fernandez-Arjona, Lucio +1 more
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