A General Neural-Networks-Based Method for Identification of Partial Differential Equations, Implemented on a Novel AI Accelerator
Mikhail A. Krinitskiy, Victor Stepanenko, Alexey O. Malkhanov, Mikhail E. Smorkalov
Abstract
Partial differential equations (PDEs) are pervasive in vast domains of science and engineering. Although there is huge legacy of numerical methods for solving direct and inverse PDE problems, these methods are computationally expensive for many fundamental and real-life applications, demanding supercomputer resources. Moreover, existing methods for PDEs identification assume concrete functional forms for the coefficients to be found, significantly limiting the range of possible solutions. The mentioned circumstances lead to increasing interest in AI-based methods for direct solving and identification of PDEs. In this study, we propose a novel method based on artificial neural networks (ANNs) for the identification of partial differential equations. The method does not require any strong a priori assumptions regarding the family of the functions approximating PDE coefficients. It allows one to approximate the coefficients of a PDE based on the observed evolution of PDE direct solution. We demonstrate efficacy and high accuracy of ANN-based method in case of diffusion equation and nonlinear diffusion-advection equation (Richards equation) applied to the simulation of heat and moisture transfer in soil. We demonstrate that the novel method implemented on Ascend platform using the mixed precision floating point operations overperforms the classical gradient descent method in Barzilai–Borwein stabilized modification (BBstab, realized on a conventional central processor), in terms of MAPE (mean absolute percentage error) and RMSE (root mean square error) of approximated coefficients at least an order of magnitude. We also found that ANN-method is much less sensitive to initial guess of parameters compared to BBstab approach. Since the considered equations are of generic form, we anticipate that the proposed ANN-based method can be successfully exploited in other applications. These potential applications include hydrodynamic-type problems, e.g., optimization of turbulence closures, where the assumed reference solutions of PDEs are usually obtained from high-resolution direct Navier-Stokes simulations.