Linear elasticity and Navier-Stokes equations are fundamental tools in material science, enabling the modeling of solid deformations and fluid flows under various conditions. These equations are widely used to simulate stresses, strains, and fluid interactions in processes like 3D printing, welding, casting, and extrusion. Physics-Informed Neural Networks (PINNs), introduced in 2019, have gained significant attention for solving complex physical problems, including fluid mechanics, wave propagation, and inverse problems. Despite their growing popularity, PINNs face challenges in training efficiency and accuracy. This paper investigates the applicability of modern PINN methodologies to material science problems involving Navier-Stokes and linear elasticity equations. For linear elasticity, a randomized selection of collocation points is employed to enhance training. For Navier-Stokes equations, hard constraints on initial and boundary conditions are implemented to avoid multi-objective optimization. These approaches aim to address training difficulties and improve PINN performance in simulating material science phenomena.