3 citations · 3 across the 8 of their papers we have counts for
4 papers · 1 filter
Time-Series Forecasting, Knowledge Distillation, and Refinement within a Multimodal PDE Foundation Model
Derek Jollie, Jingmin Sun, Zecheng Zhang +1
Symbolic encoding has been used in multi-operator learning as a way to embed additional information for distinct time-series data. For spatiotemporal systems described by time-depe…
PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics
Yuxuan Liu, Jingmin Sun, Xinjie He +3
We propose PROSE-FD, a zero-shot multimodal PDE foundational model for simultaneous prediction of heterogeneous two-dimensional physical systems related to distinct fluid dynamics…
LeMON: Learning to Learn Multi-Operator Networks
Jingmin Sun, Zecheng Zhang, Hayden Schaeffer
Single-operator learning involves training a deep neural network to learn a specific operator, whereas recent work in multi-operator learning uses an operator embedding structure t…
Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation
Jingmin Sun, Yuxuan Liu, Zecheng Zhang +1
Foundation models, such as large language models, have demonstrated success in addressing various language and image processing tasks. In this work, we introduce a multi-modal foun…