In-context operator learning with data prompts for differential equation problems.

ABSTRACT: This paper introduces the paradigm of "in-context operator learning" and the corresponding model "In-Context Operator Networks" to simultaneously learn operators from the prompted data and apply it to new questions during the inference stage, without any weight update. Existing methods are limited to using a neural network to approximate a specific equation solution or a specific operator, requiring retraining when switching to a new problem with different equations. By training a single neural network as an operator learner, rather than a solution/operator approximator, we can not only get rid of retraining (even fine-tuning) the neural network for new problems but also leverage the commonalities shared across operators so that only a few examples in the prompt are needed when learning a

SUBMITTER: Yang L 

PROVIDER: S-EPMC10523630 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature