Introduction
In here, we employ Physics-Informed Neural Networks (PINN) to blend traditional scientific modeling with modern machine learning. PINNs integrate domain-specific knowledge and physical laws into neural networks, allowing the incorporation of scientific understanding into the learning process. By harmonizing physics principles with machine learning, PINNs enhance model interpretability and generalization. This approach is especially valuable in scenarios with limited or noisy data. The synergy between scientific principles and machine learning, exemplified by PINNs, holds promise for breakthroughs in fluid dynamics, materials science, and engineering, showcasing the transformative potential of Scientific Machine Learning in research and discovery.