Exploring Physics-Informed Neural Networks: From Fundamentals to Applications in Complex Systems

Understand

Physics-informed neural networks (PINNs) have emerged as a versatile and widely applicable concept across various science and engineering domains over the past decade.

• This article offers a comprehensive overview of the fundamentals of PINNs, tracing their evolution, modifications, and various variants.
• It explores the impact of different parameters on PINNs and the optimization algorithms involved.
• The review also delves into the theoretical advancements related to the convergence, consistency, and stability of numerical solutions using PINNs, while highlighting the current state of the art.