A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks

Artificial neural networks and deep learning (68T07) Numerical optimization and variational techniques (65K10) Numerical approximation of high-dimensional functions; sparse grids (65D40)

Cites Work

Title not available (Why is that?)
Entropy viscosity method for nonlinear conservation laws
Multilayer feedforward networks are universal approximators
PPINN: parareal physics-informed neural network for time-dependent PDEs
Self-adaptive physics-informed neural networks
B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations
Parallel physics-informed neural networks via domain decomposition
Learning functional priors and posteriors from data and physics
A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data
Physics-informed neural networks for high-speed flows
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Bayesian physics informed neural networks for real-world nonlinear dynamical systems
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons
An entropy-viscosity large eddy simulation study of turbulent flow in a flexible pipe
Spectral/hp Element Methods for Computational Fluid Dynamics
Approximation by superpositions of a sigmoidal function
NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators
Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression
Residual-based attention in physics-informed neural networks
ExSpliNet: An interpretable and expressive spline-based neural network
Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems
Correcting model misspecification in physics-informed neural networks (PINNs)

This page was built for publication: A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6609808)