4 papers · 1 filter
Chain of Operators: An Inference-Time Harness for In-Context Operator Learning
Minghui Yang, Ling Guo, Chenghan Wu +1
While scientific foundation models show immense promise in accelerating physical simulations and numerical forecasting, they remain notoriously brittle when encountering out-of-dis…
Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction
Chenghan Wu, Zongmin Yu, Boai Sun +1
In-context operator learning enables neural networks to infer solution operators from contextual examples without weight updates. While prior work has demonstrated the effectivenes…
VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction
Yadi Cao, Yuxuan Liu, Liu Yang +3
In-Context Operator Networks (ICONs) have demonstrated the ability to learn operators across diverse partial differential equations using few-shot, in-context learning. However, ex…
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…