AutoIP: A United Framework to Integrate Physics into Gaussian Processes. - Da Long; Zheng Wang 042; Aditi S. Krishnapriyan; Robert M. Kirby; Shandian Zhe; Michael W. Mahoney (ICML, 2022)

A Kernel Approach for PDE Discovery and Operator Learning. - Da Long; Nicole Mrvaljevic; Shandian Zhe; Bamdad Hosseini (CoRR, 2022)

Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. - Da Long; Wei W. Xing; Aditi S. Krishnapriyan; Robert M. Kirby; Shandian Zhe; Michael W. Mahoney (AISTATS, 2024)

Solving High Frequency and Multi-Scale PDEs with Gaussian Processes. - Shikai Fang; Madison Cooley; Da Long; Shibo Li; Mike Kirby; Shandian Zhe (ICLR, 2024)

Deep peak property learning for efficient chiral molecules ECD spectra prediction. - Hao Li 073; Da Long; Li Yuan 007; Yonghong Tian 001; Xinchang Wang; Fanyang Mo (CoRR, 2024)

Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems. - Da Long; Shandian Zhe (CoRR, 2024)

Toward Efficient Kernel-Based Solvers for Nonlinear PDEs. - Zhitong Xu; Da Long; Yiming Xu; Guang Yang; Shandian Zhe; Houman Owhadi (CoRR, 2024)

Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation. - Da Long; Zhitong Xu; Guang Yang; Akil Narayan 001; Shandian Zhe (CoRR, 2024)

Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems. - Da Long; Zhitong Xu; Qiwei Yuan; Yin Yang 002; Shandian Zhe (AISTATS, 2025)

Pseudo-Physics-Informed Neural Operators: Enhancing Operator Learning from Limited Data. - Keyan Chen; Yile Li; Da Long; Zhitong Xu; Wei Xing; Jacob D. Hochhalter; Shandian Zhe (CoRR, 2025)

Spatio-temporal Fourier Transformer (StFT) for Long-term Dynamics Prediction. - Da Long; Shandian Zhe; Samuel Williams 001; Leonid Oliker; Zhe Bai (CoRR, 2025)

Decoupled peak property learning for efficient and interpretable electronic circular dichroism spectrum prediction. - Hao Li 073; Da Long; Li Yuan 007; Yu Wang; Yonghong Tian 001; Xinchang Wang; Fanyang Mo (Nat. Comput. Sci., 2025)