Spatio-temporal fluid dynamics modeling via physical-awareness and parameter diffusion guidance
H Wu, F Xu, Y Duan, Z Niu, W Wang, G Lu… - arxiv preprint arxiv …, 2024 - arxiv.org
This paper proposes a two-stage framework named ST-PAD for spatio-temporal fluid
dynamics modeling in the field of earth sciences, aiming to achieve high-precision …
dynamics modeling in the field of earth sciences, aiming to achieve high-precision …
CoPS: Empowering LLM Agents with Provable Cross-Task Experience Sharing
Sequential reasoning in agent systems has been significantly advanced by large language
models (LLMs), yet existing approaches face limitations. Reflection-driven reasoning relies …
models (LLMs), yet existing approaches face limitations. Reflection-driven reasoning relies …
[HTML][HTML] WindFormer: Learning Generic Representations for Short-Term Wind Speed Prediction
X Qiu, Y Li, JH Li, BF Wang, YL Liu - Applied Sciences, 2024 - mdpi.com
In this paper, we introduce WindFormer, an innovative transformer-based model engineered
for short-term wind speed forecasting, leveraging multivariate time series data. Unlike …
for short-term wind speed forecasting, leveraging multivariate time series data. Unlike …
Style Factorization: Explore Diverse Style Variation for Domain Generalization
Deep neural networks (DNNs) exhibit noticeable performance degradation when exposed to
out-of-distribution samples during testing. This degradation occurs due to the fact that the …
out-of-distribution samples during testing. This degradation occurs due to the fact that the …
PURE: Prompt Evolution with Graph ODE for Out-of-distribution Fluid Dynamics Modeling
This work studies the problem of out-of-distribution fluid dynamics modeling. Previous works
usually design effective neural operators to learn from mesh-based data structures …
usually design effective neural operators to learn from mesh-based data structures …