4 citations · 10 across the 14 of their papers we have counts for
8 papers · 1 filter
Same Target, Different Origins: Aligning Latent Rollout for Consistent PDE Emulation
Xinying Li, Jing Xu, Wenjun Wang +4
Neural PDE solvers commonly generate multi-step predictions through autoregressive rollout, recursively applying a learned evolution operator and feeding each prediction back as in…
Degeneracy graph determinants and higher-dimensional Young diagrams
Sanjaye Ramgoolam, Michał Studziński
Kemp and Ramgoolam proposed a basis of monomials for a commutative semisimple algebra with generators \(C_1,\ldots, C_L\), constructed from a degeneracy graph which records how the…
PMDformer: Patch-Mean Decoupling Information Transformer for Long-term Forecasting
Ao Hu, Liangjian Wen, Jiang Duan +7
Long-term time series forecasting (LTSF) plays a crucial role in fields such as energy management, finance, and traffic prediction. Transformer-based models have adopted patch-base…
CrossFlow: One-Step Generation Across Latent and Pixel Spaces
Xiyuan Wang, Xiao Zhang, Yang Li +4
Most diffusion and flow-matching generators define the prior, probability path, and prediction target in the same representation space. Latent diffusion improves efficiency by movi…
Nested Spatio-Temporal Time Series Forecasting
Yinghao Ai, Yukai Zhou, Ruoxi Jiang +8
Spatiotemporal forecasting is critical for real-world applications like traffic management, yet capturing reliable interactions remains challenging under noisy and non-stationary c…
Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security
Jinhu Qi, Muzhi Li, Jiahong Liu +9
Agentic AI systems -- Large Language Models (LLMs) augmented with planning, tool use, memory, and long-horizon interactions -- can execute complex tasks autonomously, but their mul…
Project and Generate: Divergence-Free Neural Operators for Incompressible Flows
Xigui Li, Hongwei Zhang, Ruoxi Jiang +6
Learning-based models for fluid dynamics often operate in unconstrained function spaces, leading to physically inadmissible, unstable simulations. While penalty-based methods offer…
Accelerating PDE Surrogates via RL-Guided Mesh Optimization
Yang Meng, Ruoxi Jiang, Zhuokai Zhao +3
Deep surrogate models for parametric partial differential equations (PDEs) can deliver high-fidelity approximations but remain prohibitively data-hungry: training often requires th…