Back Cover Image, Volume 5, Number 2, June 2025

Abstract

Back Cover Caption: Transfer learning in Physics-informed Neural Networks: This study explores the generalization capabilities of physics-informed neural networks (PINNs) through transfer learning techniques applied to partial differential equation (PDE) problems. Traditional PINNs require retraining when problem conditions change, whereas this approach leverages full finetuning, lightweight finetuning, and low-rank adaptation (LoRA) to enhance efficiency across varying boundary conditions, materials, and geometries. Benchmark cases include the Taylor-Green Vortex, functionally graded elastic materials, and structural problems such as a square plate with a circular hole. The results demonstrate that full finetuning and LoRA significantly improve convergence and accuracy, highlighting their potential in developing more adaptable and efficient PINN-based solvers.