Numerical Solution of First and Second Order Differential Equations with Deep Neural Networks

Ordinary differential equations (ODEs) are a fundamental tool for modeling dynamical systems in various scientific fields. However, solving ODEs analytically can be challenging, and numerical methods can be computationally expensive or inaccurate. In recent years, deep neural networks (DNNs) have emerged as a promising alternative for solving ODEs. This paper focuses on the utilization of DNNs to solve first and second-order ODEs. Existing works in this field are reviewed, with an emphasis on identifying their limitations. Subsequently, an approach is proposed to overcome these limitations. The proposed method utilizes a combination of a deep neural network and various activation functions to achieve accurate and efficient solutions for ordinary differential equations (ODEs). Deep neural networks (DNNs) have shown significant potential in solving ordinary differential equations (ODEs) due to their ability to approximate complex functions and their capacity to learn from data. Traditional numerical methods for solving ODEs have some limitations such as requiring large amounts of computation, limited accuracy, and difficulty in handling high-dimensional systems. The aim is to overcome these limitations by leveraging the capabilities of DNNs, that have the potential to learn and generalize from data, provide more accurate solutions, requiring fewer computational resources, and being able to handle high-dimensional systems. This paper aims to explore the use of DNNs in solving 1st and 2nd order differential equations, and proposes a novel approach for improving the accuracy and efficiency of the solution process.