Abstract: This talk explores the path toward foundation models for scientific computing, focusing on learning solution operators for parametric partial differential equations (PDEs). The core of the presentation showcases our contributions to overcoming these limitations: (1) A multi-modal transformer framework (PROSE) that achieves zero-shot generalization to new PDE families by jointly processing input functions and […]