In many fields, critical quantities are determined by PDEs that must be solved repeatedly for various input data. Traditional solvers require costly mesh generation and iterative numerical methods each time a new scenario is encountered. NeurOPS learns a neural operator that directly maps any input function to the corresponding PDE solution in one forward pass. In addition, this approach can handle high-dimensional data far more efficiently than traditional numerical solvers. This approach eliminates the need for repeated mesh setups and allows for rapid design exploration in engineering and fast simulation in physical sciences. NeurOPS delivers fast, accurate, and flexible PDE solutions tailored to diverse applications by learning a continuous mapping between function spaces.