We provide a new analytical approach to operator splitting for equations of the type u t = Au + uu x where A is a linear differential operator such that the equation is well-posed. Particular examples ...

Each example applies a technique that reduces N-dimensional differential systems into 1D separable parts, then solves them using exponential product formulas (Lie-Trotter, Strang, or Higher order ...

Neural networks have been widely used to solve partial differential equations (PDEs) in different fields, such as biology, physics, and materials science. Although current research focuses on PDEs ...

@inproceedings{cao2023genetic, title={Genetic Programming Symbolic Regression with Simplification-Pruning Operator for Solving Differential Equations}, author={Cao, Lulu and Zheng, Zimo and Ding, ...

Abstract: Neural operators are a class of neural networks to learn mappings between infinite-dimensional function spaces, and recent studies have shown that using neural operators to solve partial ...

ABSTRACT: The Modified Adomian Decomposition Method (MADM) is presented. A number of problems are solved to show the efficiency of the method. Further, a new solution scheme for solving boundary value ...

ABSTRACT: The Modified Adomian Decomposition Method (MADM) is presented. A number of problems are solved to show the efficiency of the method. Further, a new solution scheme for solving boundary value ...