The proposed Orthogonal Greedy Algorithm (OGA)-based neural network method can be extended to solve higher-dimensional fractional partial differential equations (PDEs) by leveraging the inherent structure of the fractional Laplacian operator in multiple dimensions. The extension involves several key steps:
Multi-dimensional Discretization: The finite difference method (FDM) used for the 1D fractional Laplacian can be generalized to higher dimensions by employing multi-dimensional grid points. This involves defining the fractional Laplacian in a multi-dimensional domain, which can be achieved using the Riemann-Liouville fractional derivative in multiple dimensions.
Higher-dimensional Neural Network Architecture: The neural network architecture can be adapted to accommodate the increased complexity of higher-dimensional function spaces. This may involve using deeper networks or more sophisticated architectures that can capture the interactions between multiple dimensions effectively.
Modification of the OGA Algorithm: The OGA algorithm must be adjusted to handle the increased dimensionality. This includes redefining the inner product and the projection steps in the context of Sobolev spaces in higher dimensions. The greedy selection process will also need to account for the multi-dimensional basis functions.
Numerical Experiments and Validation: Extensive numerical experiments should be conducted to validate the performance of the extended method. This includes testing convergence rates and error analysis in higher dimensions, ensuring that the method retains its efficiency and accuracy.
Theoretical Analysis: The theoretical guarantees and convergence rates established for the 1D case should be extended to the multi-dimensional setting. This involves proving that the OGA method converges to the true solution of the fractional PDE in higher dimensions, potentially using techniques from functional analysis and approximation theory.
By following these steps, the OGA-based neural network method can be effectively adapted to solve higher-dimensional fractional PDEs, maintaining its advantages in terms of convergence and accuracy.