Neuro-PINN: a hybrid framework for efficient nonlinear projection equation solutions (Q6569923)

scientific article; zbMATH DE number 7878909

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English	Neuro-PINN: a hybrid framework for efficient nonlinear projection equation solutions	scientific article; zbMATH DE number 7878909

Statements

instance of

scholarly article

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title

Neuro-PINN: a hybrid framework for efficient nonlinear projection equation solutions (English)

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International Journal for Numerical Methods in Engineering

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publication date

9 July 2024

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zbMATH Keywords

neurodynamic optimization

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nonlinear projection equation

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ordinary differential equation

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physics-informed neural network

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MaRDI profile type

Publication

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cites work

Finite-dimensional variational inequality and nonlinear complementarity problems: A survey of theory, algorithms and applications

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Normal Maps Induced by Linear Transformations

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A new neural network for solving nonlinear projection equations

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A collaborative neurodynamic approach to global and combinatorial optimization

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A projection neural network and its application to constrained optimization problems

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An Adaptive Continuous-Time Algorithm for Nonsmooth Convex Resource Allocation Optimization

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A one-layer recurrent neural network for nonsmooth pseudoconvex optimization with quasiconvex inequality and affine equality constraints

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Solving high-dimensional partial differential equations using deep learning

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications

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Physics-informed neural networks for high-speed flows

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Meshless physics-informed deep learning method for three-dimensional solid mechanics

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Deep learning phase‐field model for brittle fractures

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Physics-Informed Neural Networks with Hard Constraints for Inverse Design

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Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks

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Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

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A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method

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Enhanced physics‐informed neural networks for hyperelasticity

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The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems

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DeepXDE: A Deep Learning Library for Solving Differential Equations

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Complementarity problems

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Variational inequality formulation for the games with random payoffs

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A family of embedded Runge-Kutta formulae

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Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems

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Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

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On the approximation of functions by tanh neural networks

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Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs

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Identifiers

DOI

10.1002/nme.7377

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:6569923