Random Feature Models for Learning Interacting Dynamical Systems. - Yuxuan Liu 021; Scott G. McCalla; Hayden Schaeffer (CoRR, 2022)

PROSE: Predicting Operators and Symbolic Expressions using Multimodal Transformers. - Yuxuan Liu 021; Zecheng Zhang; Hayden Schaeffer (CoRR, 2023)

Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation. - Jingmin Sun; Yuxuan Liu 021; Zecheng Zhang; Hayden Schaeffer (CoRR, 2024)

PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics. - Yuxuan Liu 021; Jingmin Sun; Xinjie He; Griffin Pinney; Zecheng Zhang; Hayden Schaeffer (CoRR, 2024)

PROSE: Predicting Multiple Operators and Symbolic Expressions using multimodal transformers. - Yuxuan Liu 021; Zecheng Zhang; Hayden Schaeffer (Neural Networks, 2024)

BCAT: A Block Causal Transformer for PDE Foundation Models for Fluid Dynamics. - Yuxuan Liu 021; Jingmin Sun; Hayden Schaeffer (CoRR, 2025)

A Multimodal PDE Foundation Model for Prediction and Scientific Text Descriptions. - Elisa Negrini; Yuxuan Liu 021; Liu Yang 027; Stanley J. Osher; Hayden Schaeffer (CoRR, 2025)

AdaGrad Meets Muon: Adaptive Stepsizes for Orthogonal Updates. - Minxin Zhang; Yuxuan Liu 021; Hayden Schaeffer (CoRR, 2025)

VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction. - Yadi Cao; Yuxuan Liu 021; Liu Yang 027; Rose Yu; Hayden Schaeffer; Stanley J. Osher (Trans. Mach. Learn. Res., 2026)

Adam Improves Muon: Adaptive Moment Estimation with Orthogonalized Momentum. - Minxin Zhang; Yuxuan Liu 021; Hayden Schaeffer (CoRR, 2026)