Four-dimensional flow MRI enables non-invasive, patient-specific assessment of cerebrovascular hemodynamics. However, limitations related to inherent tradeoffs between resolution and noise reduces the accuracy of derived parameters such as pressure gradients and wall shear stress. We introduce a Physics-Informed Implicit Neural Representation (PI-INR) framework for unsupervised super-resolution and denoising of 4D Flow MRI. We build on the WIRE architecture, leveraging its controlled spectral bias to capture fine-scale flow details while reducing noise, and introduce an anti-aliasing loss recover flow from aliased regions. Throught this proposal, we will seek to extend this work and evaluate a variety of data-driven physics-informed approaches for improved performance.