Physics-Informed Neural Networks: Solving Physics Equations with Deep Learning
2025-02-17
This article introduces a novel method for solving physics equations using Physics-Informed Neural Networks (PINNs). Unlike traditional supervised learning, PINNs directly use the differential equation as a loss function, leveraging the powerful function approximation capabilities of neural networks to learn the solution to the equation. The author demonstrates the application of PINNs in solving different types of differential equations using the simple harmonic oscillator and heat equation as examples. Comparisons with traditional numerical methods show that PINNs can achieve high-accuracy solutions with limited training data, especially advantageous when dealing with complex geometries.
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