Since its formulation by Sir Isaac Newton, the problem of solving the equations of motion for
three bodies under their own gravitational force has remained practically unsolved …

This paper explores the difficulties in solving partial differential equations (PDEs) using
physics-informed neural networks (PINNs). PINNs use physics as a regularization term in …

A recent method for solving differential equations using feedforward neural networks was
applied to a non-steady fixed bed non-catalytic solid–gas reactor. As neural networks have …

Solving a multivariable static Schrödinger equation for a quantum system, to produce
multiple excited-state energy eigenvalues and wave functions, is one of the basic tasks in …

We introduce the Optimizing a Discrete Loss (ODIL) framework for the numerical solution of
Partial Differential Equations (PDE) using machine learning tools. The framework formulates …

A feedforward neural network has a remarkable property which allows the network itself to
be a universal approximator for any function. Here we present a universal machine-learning …

In recent years, advances in computing hardware and computational methods have
prompted a wealth of activities for solving inverse problems in physics. These problems are …

We devise a deep learning solver inspired by physics-informed neural networks (PINNs) to
tackle the polymer self-consistent field theory (SCFT) equations for one-dimensional AB …

In this study a new method based on neural network has been developed for solution of
differential equations. A modified neural network is used to solve the Burger's equation in …

Extrapolation to predict unseen data outside the training distribution is a common challenge
in real-world scientific applications of physics and chemistry. However, the extrapolation …