Neural network representation of the probability density function of diffusion processes
This work addresses the challenge of modeling stochastic dynamical systems for researchers in physics and engineering, though it appears incremental as it applies existing neural network methods to known differential equations.
The paper tackled the problem of characterizing the state of dynamical systems in random environments by developing physics-informed neural networks to approximate probability density functions or characteristic functions, showing that these networks can effectively solve complex differential equations like the Fokker-Planck equation and integro-differential equations under various noise types.
Physics-informed neural networks are developed to characterize the state of dynamical systems in a random environment. The neural network approximates the probability density function (pdf) or the characteristic function (chf) of the state of these systems which satisfy the Fokker-Planck equation or an integro-differential equation under Gaussian and/or Poisson white noises. We examine analytically and numerically the advantages and disadvantages of solving each type of differential equation to characterize the state. It is also demonstrated how prior information of the dynamical system can be exploited to design and simplify the neural network architecture. Numerical examples show that: 1) the neural network solution can approximate the target solution even for partial integro-differential equations and system of PDEs describing the time evolution of the pdf/chf, 2) solving either the Fokker-Planck equation or the chf differential equation using neural networks yields similar pdfs of the state, and 3) the solution to these differential equations can be used to study the behavior of the state for different types of random forcings.