Deep neural network for solving differential equations motivated by Legendre-Galerkin approximation
DOI10.4208/IJNAM2024-1026MaRDI QIDQ6648518
John O. Morgan, Hyun-Woo Kim, Bryce Chudomelka, Youngjoon Hong, Jinyoung Park
Publication date: 4 December 2024
Published in: International Journal of Numerical Analysis and Modeling (Search for Journal in Brave)
spectral element methodneural networkdeep learningLegendre-Galerkin methoddata driven numerical method
Artificial neural networks and deep learning (68T07) Spectral, collocation and related methods for boundary value problems involving PDEs (65N35) Numerical solution of discretized equations for boundary value problems involving PDEs (65N22) Numerical algorithms for specific classes of architectures (65Y10) Computational aspects of data analysis and big data (68T09)
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Enriched numerical scheme for singularly perturbed barotropic quasi-geostrophic equations
- A high-order perturbation of surfaces method for scattering of linear waves by periodic multiply layered gratings in two and three dimensions
- Hidden physics models: machine learning of nonlinear partial differential equations
- Multilayer feedforward networks are universal approximators
- The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
- A high-order perturbation of surfaces method for vector electromagnetic scattering by doubly layered periodic crossed gratings
- DGM: a deep learning algorithm for solving partial differential equations
- On generalized residual network for deep learning of unknown dynamical systems
- Weak form theory-guided neural network (TgNN-wf) for deep learning of subsurface single- and two-phase flow
- On quadrature rules for solving partial differential equations using neural networks
- On the numerical approximations of stiff convection-diffusion equations in a circle
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Spectral Methods
- The Relationship between Variable Selection and Data Agumentation and a Method for Prediction
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- EnResNet: ResNets Ensemble via the Feynman--Kac Formalism for Adversarial Defense and Beyond
- DeepXDE: A Deep Learning Library for Solving Differential Equations
- Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions
- Learning data-driven discretizations for partial differential equations
- The Accuracy of the Chebyshev Differencing Method for Analytic Functions
- Approximation by superpositions of a sigmoidal function
This page was built for publication: Deep neural network for solving differential equations motivated by Legendre-Galerkin approximation
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6648518)