Matrix Lie Maps and Neural Networks for Solving Differential Equations
This provides a more efficient method for simulating and identifying dynamical systems in physics and engineering, though it appears incremental as it builds on existing Lie map and neural network concepts.
The paper tackles solving differential equations by linking polynomial neural networks to matrix Lie maps, enabling direct weight calculation from equations or data-driven learning without training, and demonstrates this on the Van der Pol oscillator and Burgers' equation.
The coincidence between polynomial neural networks and matrix Lie maps is discussed in the article. The matrix form of Lie transform is an approximation of the general solution of the nonlinear system of ordinary differential equations. It can be used for solving systems of differential equations more efficiently than traditional step-by-step numerical methods. Implementation of the Lie map as a polynomial neural network provides a tool for both simulation and data-driven identification of dynamical systems. If the differential equation is provided, training a neural network is unnecessary. The weights of the network can be directly calculated from the equation. On the other hand, for data-driven system learning, the weights can be fitted without any assumptions in view of differential equations. The proposed technique is discussed in the examples of both ordinary and partial differential equations. The building of a polynomial neural network that simulates the Van der Pol oscillator is discussed. For this example, we consider learning the dynamics from a single solution of the system. We also demonstrate the building of the neural network that describes the solution of Burgers' equation that is a fundamental partial differential equation.