Investigate the geometric structure of the 10-dimensional latent space generated by a PINN for the 2D Burger's equation across 25 different initial conditions. For each initial condition, analyze the set of latent vectors $\{L(x,t,IC) \mid x \in \text{grid}, t \in \text{grid}\}$ as a potential low-dimensional manifold embedded in $\mathbb{R}^{10}$. Compare and contrast these 25 manifolds to determine if they share common geometric properties, are related by simple transformations (e.g., translations, rotations, low-dimensional variations), or collectively lie within a simpler, overarching structure. This study aims to uncover how the PINN encodes the effects of initial conditions within its latent representation, potentially yielding a novel and simple description of the solution manifold's structure as learned by the network.