# Physics Informed Extreme Learning Machine (PIELM) -- A rapid method for the numerical solution of partial differential equations

## TL;DR

PIELM is a fast, physics-informed machine learning method that efficiently solves linear partial differential equations, matching or surpassing PINNs in accuracy and offering a scalable distributed version for large domains.

## Contribution

This paper introduces PIELM, a rapid physics-informed neural network alternative for PDEs, and proposes DPIELM, a distributed extension for large-scale problems.

## Key findings

- PIELM achieves comparable or better accuracy than PINNs.
- DPIELM provides effective solutions for large domain PDEs.
- Neural network approaches can be competitive with traditional methods.

## Abstract

There has been rapid progress recently on the application of deep networks to the solution of partial differential equations, collectively labelled as Physics Informed Neural Networks (PINNs). In this paper, we develop Physics Informed Extreme Learning Machine (PIELM), a rapid version of PINNs which can be applied to stationary and time dependent linear partial differential equations. We demonstrate that PIELM matches or exceeds the accuracy of PINNs on a range of problems. We also discuss the limitations of neural network based approaches, including our PIELM, in the solution of PDEs on large domains and suggest an extension, a distributed version of our algorithm -{}- DPIELM. We show that DPIELM produces excellent results comparable to conventional numerical techniques in the solution of time-dependent problems. Collectively, this work contributes towards making the use of neural networks in the solution of partial differential equations in complex domains as a competitive alternative to conventional discretization techniques.