A neural networks-based numerical method for the generalized Caputo-type fractional differential equations

Cites Work

Unnamed Item
Unnamed Item
Unnamed Item
Approximation solution of fractional partial differential equations by neural networks
The theory of connections: connecting points
Solving differential equations of fractional order using an optimization technique based on training artificial neural network
Analysis of Timoshenko-Ehrenfest beam problems using the theory of functional connections
Numerical solution of systems of fractional delay differential equations using a new kind of wavelet basis
Least-squares solutions of boundary-value problems in hybrid systems
Theory of functional connections applied to quadratic and nonlinear programming under equality constraints
Solution of a COVID-19 model via new generalized Caputo-type fractional derivatives
Analytic solution for the strongly nonlinear multi-order fractional version of a BVP occurring in chemical reactor theory
Numerical simulation of initial value problems with generalized Caputo-type fractional derivatives
Neural network solution of single-delay differential equations
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Approximation via statistical convergence in the sense of power series method of Bögel-type continuous functions
A new artificial neural network structure for solving high-order linear fractional differential equations
fPINNs: Fractional Physics-Informed Neural Networks
Analysis of fractional differential equations
High-order approximation to Caputo derivatives and Caputo-type advection-diffusion equations. III.
A novel numerical scheme for fractional differential equations using extreme learning machine