Solving Falkner-Skan type equations via Legendre and Chebyshev Neural
Blocks
- URL: http://arxiv.org/abs/2308.03337v1
- Date: Mon, 7 Aug 2023 06:38:59 GMT
- Title: Solving Falkner-Skan type equations via Legendre and Chebyshev Neural
Blocks
- Abstract summary: A new deep-learning architecture for solving the non-linear Falkner-Skan equation is proposed.
The efficiency of the proposed method is carried out by various configurations of the Falkner-Skan equation.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, a new deep-learning architecture for solving the non-linear
Falkner-Skan equation is proposed. Using Legendre and Chebyshev neural blocks,
this approach shows how orthogonal polynomials can be used in neural networks
to increase the approximation capability of artificial neural networks. In
addition, utilizing the mathematical properties of these functions, we overcome
the computational complexity of the backpropagation algorithm by using the
operational matrices of the derivative. The efficiency of the proposed method
is carried out by simulating various configurations of the Falkner-Skan
equation.
Related papers
- DInf-Grid: A Neural Differential Equation Solver with Differentiable Feature Grids [73.28614344779076]
We present a differentiable grid-based representation for efficiently solving differential equations (DEs)<n>Our results demonstrate a 5-20x speed-up over coordinate-based methods, solving differential equations in seconds or minutes while maintaining comparable accuracy and compactness.
arXiv Detail & Related papers (2026-01-15T18:59:57Z) - Solving Time-Fractional Partial Integro-Differential Equations Using Tensor Neural Network [0.0]
We propose a novel machine learning method based on adaptive tensor neural network subspace to solve linear time-fractional diffusion-wave equations.
Some numerical examples are provided to validate the efficiency and accuracy of the proposed tensor neural network based machine learning method.
arXiv Detail & Related papers (2025-04-02T07:50:23Z) - Chebyshev Spectral Neural Networks for Solving Partial Differential Equations [0.0]
The study uses a feedforward neural network model and error backpropagation principles, utilizing automatic differentiation (AD) to compute the loss function.
The numerical efficiency and accuracy of the CSNN model are investigated through testing on elliptic partial differential equations, and it is compared with the well-known Physics-Informed Neural Network(PINN) method.
arXiv Detail & Related papers (2024-06-06T05:31:45Z) - Front-propagation Algorithm: Explainable AI Technique for Extracting Linear Function Approximations from Neural Networks [0.0]
This paper introduces the front-propagation algorithm, a novel AI technique designed to elucidate the decision-making logic of deep neural networks.
Unlike other popular explainability algorithms such as Integrated Gradients or Shapley Values, the proposed algorithm is able to extract an accurate and consistent linear function explanation of the network.
We demonstrate its efficacy in providing accurate linear functions with three different neural network architectures trained on publicly available benchmark datasets.
arXiv Detail & Related papers (2024-05-25T14:50:23Z) - Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
We propose to represent neural networks as computational graphs of parameters.
Our approach enables a single model to encode neural computational graphs with diverse architectures.
We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
arXiv Detail & Related papers (2024-03-18T18:01:01Z) - The Convex Landscape of Neural Networks: Characterizing Global Optima
and Stationary Points via Lasso Models [75.33431791218302]
Deep Neural Network Network (DNN) models are used for programming purposes.
In this paper we examine the use of convex neural recovery models.
We show that all the stationary non-dimensional objective objective can be characterized as the standard a global subsampled convex solvers program.
We also show that all the stationary non-dimensional objective objective can be characterized as the standard a global subsampled convex solvers program.
arXiv Detail & Related papers (2023-12-19T23:04:56Z) - Permutation Equivariant Neural Functionals [92.0667671999604]
This work studies the design of neural networks that can process the weights or gradients of other neural networks.
We focus on the permutation symmetries that arise in the weights of deep feedforward networks because hidden layer neurons have no inherent order.
In our experiments, we find that permutation equivariant neural functionals are effective on a diverse set of tasks.
arXiv Detail & Related papers (2023-02-27T18:52:38Z) - A Recursively Recurrent Neural Network (R2N2) Architecture for Learning
Iterative Algorithms [64.3064050603721]
We generalize Runge-Kutta neural network to a recurrent neural network (R2N2) superstructure for the design of customized iterative algorithms.
We demonstrate that regular training of the weight parameters inside the proposed superstructure on input/output data of various computational problem classes yields similar iterations to Krylov solvers for linear equation systems, Newton-Krylov solvers for nonlinear equation systems, and Runge-Kutta solvers for ordinary differential equations.
arXiv Detail & Related papers (2022-11-22T16:30:33Z) - Partial Differential Equations is All You Need for Generating Neural Architectures -- A Theory for Physical Artificial Intelligence Systems [40.20472268839781]
We generalize the reaction-diffusion equation in statistical physics, Schr"odinger equation in quantum mechanics, Helmholtz equation in paraxial optics.
We take finite difference method to discretize NPDE for finding numerical solution.
Basic building blocks of deep neural network architecture, including multi-layer perceptron, convolutional neural network and recurrent neural networks, are generated.
arXiv Detail & Related papers (2021-03-10T00:05:46Z) - Deep neural network for solving differential equations motivated by
Legendre-Galerkin approximation [16.64525769134209]
We explore the performance and accuracy of various neural architectures on both linear and nonlinear differential equations.
We implement a novel Legendre-Galerkin Deep Neural Network (LGNet) algorithm to predict solutions to differential equations.
arXiv Detail & Related papers (2020-10-24T20:25:09Z) - Connecting Weighted Automata, Tensor Networks and Recurrent Neural
Networks through Spectral Learning [58.14930566993063]
We present connections between three models used in different research fields: weighted finite automata(WFA) from formal languages and linguistics, recurrent neural networks used in machine learning, and tensor networks.
We introduce the first provable learning algorithm for linear 2-RNN defined over sequences of continuous vectors input.
arXiv Detail & Related papers (2020-10-19T15:28:00Z) - Multipole Graph Neural Operator for Parametric Partial Differential
Equations [57.90284928158383]
One of the main challenges in using deep learning-based methods for simulating physical systems is formulating physics-based data.
We propose a novel multi-level graph neural network framework that captures interaction at all ranges with only linear complexity.
Experiments confirm our multi-graph network learns discretization-invariant solution operators to PDEs and can be evaluated in linear time.
arXiv Detail & Related papers (2020-06-16T21:56:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.