An important application of neural networks in scientific computing is non-linear operator learning. In this framework, a neural network is trained to fit a non-linear map between infinite dimensional spaces, for example, the solution operator of ordinary and partial differential equations. Recently, inspired by the discovery of in-context learning for large language models, an even more ambitious paradigm has been explored, called multi-operator learning. In this approach, a neural network is trained to learn many different operators at the same time. At inference time, the network is passed a small number of example input and output pairs to disambiguate the desired operator. In this work, we provide a precise mathematical formulation of the multi-operator learning problem, propose a parameter-efficient, attention-free architecture, called DeepOSets, and prove its universality in the in-context multi-operator learning task. We perform experiments demonstrating the efficacy of DeepOSets on several benchmarks, including Burgers' equation, 1D and 2D Darcy flow, and a variety of initial-value and boundary-value differential equations. In a comparison with the transformer-based ICON model on the 1D Darcy problem, under a small training budget (10k steps), DeepOSets attained lower error at every context size with 19% of the parameters, while with an extended budget (100k steps), ICON attains 26-41% lower error, but at the cost of roughly 218$\times$ more training time, 12$\times$ higher inference latency, and 5.3$\times$ more parameters.

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