Testing Physics-Informed Neural Networks Against Classical Numerical Methods
A comparative experiment pits physics-informed neural networks (PINNs) against traditional finite-difference solvers across one-dimensional and five-dimensional problems. The results reveal how dimensionality alters the performance balance between machine learning approaches and classical numerical techniques, providing practical insights for researchers choosing between these computational methods.
Source: Towards Data Science