Multi Operator Learning

Multi-operator learning (MOL) aims to train a single neural network to approximate multiple mathematical operators, unlike traditional single-operator learning which trains separate networks for each operator. Current research focuses on developing efficient MOL architectures, often leveraging foundation models and distributed training methods to handle large datasets and diverse operator families, including applications to solving partial differential equations (PDEs). This approach offers the potential for improved efficiency and generalization in scientific computing, enabling more accurate and data-efficient solutions to complex problems across various scientific disciplines. The ability to learn and extrapolate from multiple operators also enhances the predictive capabilities of these models.