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.IR2025
iTRI-QA: a Toolset for Customized Question-Answer Dataset Generation Using Language Models for Enhanced Scientific Research
Qiming Liu, Zhongzheng Niu, Siting Liu +1
The exponential growth of AI in science necessitates efficient and scalable solutions for retrieving and preserving research information. Here, we present a tool for the developmen…