Our @NatRevPhys perspective article on neural operators and their ability to accelerate simulations and design is now out. rdcu.be/dD8BI @Nature 1. Neural operators learn mappings between functions, e.g. spatiotemporal processes and partial differential equations. Hence, they can extrapolate and predict solutions at new locations unseen during training. 2. They can be integrated with physics and other domain constraints enforced at finer resolutions to obtain high-fidelity solutions and good generalization. 3. They are differentiable, so they can directly optimize parameters for inverse design and other inverse problems. Neural operators can therefore augment, or even replace, existing numerical simulators in many applications, such as computational fluid dynamics, weather forecasting and material modeling, providing speedups of 4-5 orders of magnitude. @Azizzadenesheli @ZongyiLiCaltech @JeanKossaifi @caltech