2.8k citations · 8.6k across the 72 of their papers we have counts for
5 papers · 1 filter
HelmFluid: Learning Helmholtz Dynamics for Interpretable Fluid Prediction
Lanxiang Xing, Haixu Wu, Yuezhou Ma +2
Fluid prediction is a long-standing challenge due to the intrinsic high-dimensional non-linear dynamics. Previous methods usually utilize the non-linear modeling capability of deep…
HarmonyDream: Task Harmonization Inside World Models
Haoyu Ma, Jialong Wu, Ningya Feng +5
Model-based reinforcement learning (MBRL) holds the promise of sample-efficient learning by utilizing a world model, which models how the environment works and typically encompasse…
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
Yong Liu, Chenyu Li, Jianmin Wang +1
Real-world time series are characterized by intrinsic non-stationarity that poses a principal challenge for deep forecasting models. While previous models suffer from complicated s…
CLIPood: Generalizing CLIP to Out-of-Distributions
Yang Shu, Xingzhuo Guo, Jialong Wu +3
Out-of-distribution (OOD) generalization, where the model needs to handle distribution shifts from training, is a major challenge of machine learning. Contrastive language-image pr…
Solving High-Dimensional PDEs with Latent Spectral Models
Haixu Wu, Tengge Hu, Huakun Luo +2
Deep models have achieved impressive progress in solving partial differential equations (PDEs). A burgeoning paradigm is learning neural operators to approximate the input-output m…