Cover Image

An Orthogonal Polynomial Kernel-Based Machine Learning Model for Differential-Algebraic Equations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Taheri, Tayebeh, Aghaei, Alireza Afzal, Parand, Kourosh
Format:	Preprint
Published:	2024
Subjects:	Numerical Analysis Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Accelerating Fractional PINNs using Operational Matrices of Derivative
by: Taheri, Tayebeh, et al.
Published: (2024)

A Physics-Informed Machine Learning Approach for Solving Distributed Order Fractional Differential Equations
by: Aghaei, Alireza Afzal
Published: (2024)

fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions
by: Aghaei, Alireza Afzal
Published: (2024)

PINNIES: An Efficient Physics-Informed Neural Network Framework to Integral Operator Problems
by: Aghaei, Alireza Afzal, et al.
Published: (2024)

KANtrol: A Physics-Informed Kolmogorov-Arnold Network Framework for Solving Multi-Dimensional and Fractional Optimal Control Problems
by: Aghaei, Alireza Afzal
Published: (2024)

rKAN: Rational Kolmogorov-Arnold Networks
by: Aghaei, Alireza Afzal
Published: (2024)

Hermite Neural Network Simulation for Solving the 2D Schrodinger Equation
by: Parand, Kourosh, et al.
Published: (2024)

Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning
by: Chen, Jiale, et al.
Published: (2024)

Variational (Energy-Based) Spectral Learning: A Machine Learning Framework for Solving Partial Differential Equations
by: Hammad, M. M.
Published: (2026)

Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis
by: Jalalian, Yasamin, et al.
Published: (2025)

Advanced Physics-Informed Neural Network with Residuals for Solving Complex Integral Equations
by: Moghaddam, Mahdi Movahedian, et al.
Published: (2025)

Machine Learning Based Optimization Workflow for Tuning Numerical Settings of Differential Equation Solvers for Boundary Value Problems
by: Victor, Viny Saajan, et al.
Published: (2024)

A Hybrid Kernel-Free Boundary Integral Method with Operator Learning for Solving Parametric Partial Differential Equations In Complex Domains
by: Ling, Shuo, et al.
Published: (2024)

BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
by: Mirzabeigi, Elmira, et al.
Published: (2025)

An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
by: Gonon, Lukas, et al.
Published: (2024)

Scale-Consistent Learning for Partial Differential Equations
by: Li, Zongyi, et al.
Published: (2025)

BEACONS: Bounded-Error, Algebraically-Composable Neural Solvers for Partial Differential Equations
by: Gorard, Jonathan, et al.
Published: (2026)

Hyperparameter optimization of orthogonal functions in the numerical solution of differential equations
by: Alireza Afzal Aghaei, et al.
Published: (2024)

Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation
by: Sun, Jingmin, et al.
Published: (2024)

Constrained or Unconstrained? Neural-Network-Based Equation Discovery from Data
by: Norman, Grant, et al.
Published: (2024)

Solving Partial Differential Equations with Random Feature Models
by: Liao, Chunyang
Published: (2024)

Self-Supervised Learning with Lie Symmetries for Partial Differential Equations
by: Mialon, Grégoire, et al.
Published: (2023)

Fine-Tune Language Models as Multi-Modal Differential Equation Solvers
by: Yang, Liu, et al.
Published: (2023)

Digital Twin Data Modelling by Randomized Orthogonal Decomposition and Deep Learning
by: Bistrian, Diana Alina, et al.
Published: (2022)

Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations
by: Chen, Chuqi, et al.
Published: (2024)

AE-ViT: Stable Long-Horizon Parametric Partial Differential Equations Modeling
by: Mikuš, Iva, et al.
Published: (2026)

Data-Driven Self-Supervised Learning for the Discovery of Solution Singularity for Partial Differential Equations
by: Cai, Difeng, et al.
Published: (2025)

FBSJNN: A Theoretically Interpretable and Efficiently Deep Learning method for Solving Partial Integro-Differential Equations
by: Ye, Zaijun, et al.
Published: (2024)

HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
by: Bryutkin, Andrey, et al.
Published: (2024)

Physics-embedded Fourier Neural Network for Partial Differential Equations
by: Xu, Qingsong, et al.
Published: (2024)

Component Fourier Neural Operator for Singularly Perturbed Differential Equations
by: Li, Ye, et al.
Published: (2024)

Deep Parallel Spectral Neural Operators for Solving Partial Differential Equations with Enhanced Low-Frequency Learning Capability
by: Ma, Qinglong, et al.
Published: (2024)

Randomized Neural Networks for Partial Differential Equation on Static and Evolving Surfaces
by: Sun, Jingbo, et al.
Published: (2026)

Random Neural Network Expressivity for Non-Linear Partial Differential Equations
by: Mehmood, Muhammed Ali, et al.
Published: (2026)

Finite Expression Method for Solving High-Dimensional Partial Differential Equations
by: Liang, Senwei, et al.
Published: (2022)

Randomized Neural Networks for Integro-Differential Equations with Application to Neutron Transport
by: Dang, Haoning, et al.
Published: (2026)

KROM: Kernelized Reduced Order Modeling
by: Bacho, Aras, et al.
Published: (2026)

Neural Operators with Localized Integral and Differential Kernels
by: Liu-Schiaffini, Miguel, et al.
Published: (2024)

DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training
by: Yang, Yahong
Published: (2024)

Solving High-Dimensional Partial Integral Differential Equations: The Finite Expression Method
by: Hardwick, Gareth, et al.
Published: (2024)