Context Operator

In-context operator learning aims to create single, adaptable neural network models capable of solving diverse differential equation problems without retraining for each new equation. Current research focuses on developing architectures, such as In-Context Operator Networks (ICON), that learn operators from example data "prompts" and apply this knowledge to unseen problems during inference. This approach leverages the shared structure across different equations, reducing the need for extensive training data and potentially revolutionizing scientific computing by enabling the creation of general-purpose solvers for various scientific and engineering applications. Recent work also explores incorporating human knowledge, expressed through natural language descriptions, to improve model performance and reduce data requirements.