wuhx23@mails.tsinghua.edu.cn
I am currently a Ph.D. student (from fall, 2020) in School of Software, Tsinghua University, under the supervision of Prof. Mingsheng Long.
My research interests lie in deep and scientific learning, especially science-inspired model architectures. My research goal is to model this ever-changing and non-stationary world through scientific and interpretable deep models. Besides, I also devote myself to promoting research to valuable real-world applications.
Google Scholar / Semantic Scholar / GitHub
Transolver: A Fast Transformer Solver for PDEs on General Geometries
Haixu Wu, Huakun Luo, Haowen Wang, Jianmin Wang, Mingsheng Long#
arXiv 2024
EuLagNet: Eulerian Fluid Prediction with Lagrangian Dynamics
Qilong Ma*
, Haixu Wu*
, Lanxiang Xing, Jianmin Wang, Mingsheng Long#
arXiv 2024
TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling
Jiaxiang Dong*
, Haixu Wu*
, Yuxuan Wang, Yunzhong Qiu, Li Zhang, Jianmin Wang, Mingsheng Long#
arXiv 2024
TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
Yuxuan Wang*
, Haixu Wu*
, Jiaxiang Dong, Yong Liu, Yunzhong Qiu, Haoran Zhang, Jianmin Wang, Mingsheng Long#
arXiv 2024
HelmFluid: Learning Helmholtz Dynamics for Interpretable Fluid Prediction
Lanxiang Xing*
, Haixu Wu*
, Yuezhou Ma, Jianmin Wang, Mingsheng Long#
arXiv 2023
Interpretable Weather Forecasting for Worldwide Stations with a Unified Deep Model
Haixu Wu, Hang Zhou, Mingsheng Long#, Jianmin Wang#
Nature Machine Intelligence 2023 / PDF / Code / Slides (Cover Paper)
PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning
Yunbo Wang*
, Haixu Wu*
, Jianjin Zhang, Zhifeng Gao, Jianmin Wang, Philip S. Yu, Mingsheng Long#
TPAMI 2022 / PDF / Code (ESI Highly Cited Paper)
ModeRNN: Harnessing Spatiotemporal Mode Collapse in Unsupervised Predictive Learning
Zhiyu Yao, Yunbo Wang, Haixu Wu, Jianmin Wang, Mingsheng Long#
TPAMI 2023 / PDF / Code
iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
Yong Liu*
, Tengge Hu*
, Haoran Zhang*
, Haixu Wu, Shiyu Wang, Lintao Ma, Mingsheng Long#
ICLR 2024 / PDF / Code (Spotlight)
TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting
Shiyu Wang*
, Haixu Wu*
, Xiaoming Shi, Tengge Hu, Huakun Luo, Lintao Ma, James Y. Zhang, Jun Zhou
ICLR 2024 / PDF / Code / Slides
SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling
Jiaxiang Dong*
, Haixu Wu*
, Haoran Zhang, Li Zhang, Jianmin Wang, Mingsheng Long#
NeurIPS 2023 / PDF / Code (Spotlight)
Solving High-Dimensional PDEs with Latent Spectral Models
Haixu Wu, Tengge Hu, Huakun Luo, Jianmin Wang, Mingsheng Long#
ICML 2023 / PDF / Code / Slides
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
Haixu Wu*
, Tengge Hu*
, Yong Liu*
, Hang Zhou, Jianmin Wang, Mingsheng Long#
ICLR 2023 / PDF / Code / Slides
Non-stationary Transformers: Rethinking the Stationarity in Time Series Forecasting
Yong Liu*
, Haixu Wu*
, Jianmin Wang, Mingsheng Long#
NeurIPS 2022 / PDF / Code
Supported Policy Optimization for Offline Reinforcement Learning
Jialong Wu, Haixu Wu, Zihan Qiu, Jianmin Wang, Mingsheng Long#
NeurIPS 2022 / PDF / Code / Slides
Flowformer: Linearizing Transformers with Conservation Flows
Haixu Wu, Jialong Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long#
ICML 2022 / PDF / Code / Slides
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
Jiehui Xu*
, Haixu Wu*
, Jianmin Wang, Mingsheng Long#
ICLR 2022 / PDF / Code / Slides (Spotlight)
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long#
NeurIPS 2021 / PDF / Code / Slides (Rank 14th in NeurIPS 2021)
MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions
Haixu Wu*
, Zhiyu Yao*
, Jianmin Wang, Mingsheng Long#
CVPR 2021 / PDF / Appendix / Code / Slides
* Equal Contribution, # Corresponding Author