2 papers
stat.ML2025
Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations
Benjamin J. Zhang, Siting Liu, Stanley J. Osher +1
In-context operator networks (ICON) are a class of operator learning methods based on the novel architectures of foundation models. Trained on a diverse set of datasets of initial…
cs.LG2025
A Multimodal PDE Foundation Model for Prediction and Scientific Text Descriptions
Elisa Negrini, Yuxuan Liu, Liu Yang +2
Neural networks are one tool for approximating non-linear differential equations used in scientific computing tasks such as surrogate modeling, real-time predictions, and optimal c…