1 citations · 1 across the 8 of their papers we have counts for
11 papers
Less Data, Better Timing: Student-Curriculum Coupling for VLM On-Policy Distillation in Temporal Video Grounding
Jiacheng Qiu, Yunsoo Kim, Ruichen Xu +3
On-policy distillation (OPD) provides dense supervision directly on student-generated trajectories, making it an effective post-training strategy for vision-language models in temp…
SCOPE: Observation-Conditioned Full-Target Prediction for Sparse PDE Inference
Ruichen Xu, Siyao Wang, Fang Wan +8
Recovering complete physical fields from sparse observations is challenging because the measurements may not uniquely determine the underlying state. Diffusion-based PDE solvers ad…
PDE-OBS: Controlled Evaluation Across Observation Patterns
Ruichen Xu, Siyao Wang, Fang Wan +7
Physical-field reconstruction and forecasting depend on both measurement density and spatial layout, yet evaluation under a single observation pattern does not characterize perform…
HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning
Ruichen Xu, Jingxiang Qu, Wenhan Gao +5
Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architectures (JEPAs) avoid explicit neg…
Towards Principled Test-Time Adaptation for Time Series Forecasting
Haochun Wang, Ruichen Xu, Georgios Kementzidis +3
Test-time adaptation (TTA) has recently emerged as a promising approach for improving time series forecasting (TSF) under distribution shift. Existing TSF-TTA methods differ in how…
Can Data-Driven Dynamics Reveal Hidden Physics? There Is A Need for Interpretable Neural Operators
Wenhan Gao, Jian Luo, Fang Wan +4
Recently, neural operators have emerged as powerful tools for learning mappings between function spaces, enabling data-driven simulations of complex dynamics. Despite their success…
Boundary-Informed Method of Lines for Physics Informed Neural Networks
Maximilian Cederholm, Siyao Wang, Haochun Wang +2
We propose a hybrid solver that fuses the dimensionality-reduction strengths of the Method of Lines (MOL) with the flexibility of Physics-Informed Neural Networks (PINNs). Instead…
Kolmogorov-Arnold Representation for Symplectic Learning: Advancing Hamiltonian Neural Networks
Zongyu Wu, Ruichen Xu, Luoyao Chen +3
We propose a Kolmogorov-Arnold Representation-based Hamiltonian Neural Network (KAR-HNN) that replaces the Multilayer Perceptrons (MLPs) with univariate transformations. While Hami…
An Iterative Framework for Generative Backmapping of Coarse Grained Proteins
Georgios Kementzidis, Erin Wong, John Nicholson +2
The techniques of data-driven backmapping from coarse-grained (CG) to fine-grained (FG) representation often struggle with accuracy, unstable training, and physical realism, especi…
Velocity-Inferred Hamiltonian Neural Networks: Learning Energy-Conserving Dynamics from Position-Only Data
Ruichen Xu, Zongyu Wu, Luoyao Chen +5
Data-driven modeling of physical systems often relies on learning both positions and momenta to accurately capture Hamiltonian dynamics. However, in many practical scenarios, only…
The Impact of Move Schemes on Simulated Annealing Performance
Ruichen Xu, Haochun Wang, Yuefan Deng
Designing an effective move-generation function for Simulated Annealing (SA) in complex models remains a significant challenge. In this work, we present a combination of theoretica…