Abstract: Spectrum analysis provides one of the most effective paradigms for information-preserving dimensionality reduction in data: often, a simple description of naturally occurring signals can be obtained via few terms of periodic basis functions. Neural operators designed for frequency domain learning are based on complex-valued transforms i.e. Fourier Transforms (FT), and layers that perform computation on the spectrum and input data separately. This design introduces considerable computational overhead: for each layer, a forward and inverse FT. Instead, this work introduces a blueprint for frequency domain learning through a single transform: transform once (T1). To enable efficient, direct learning in the frequency domain we develop a variance preserving weight initialization scheme and address the open problem of choosing a transform. Our results significantly streamline the design process of neural operators, pruning redundant transforms, and leading to speedups of 3 x to 30 that increase with data resolution and model size. We perform extensive experiments on learning to solve partial differential equations, including incompressible Navier-Stokes, turbulent flows around airfoils, and high-resolution video of smoke dynamics. T1 models improve on the test performance of SOTA neural operators while requiring significantly less computation, with over $30\%$ reduction in predictive error across tasks.