2 citations · 3 across the 11 of their papers we have counts for
7 papers · 1 filter
Rollback-Free Stable Brick Structures Generation
Chenhui Xu, Ziyue Bai, Fuxun Yu +2
While autoregressive models have advanced 3D generation, creating physically stable brick structures remains a challenge due to the strict requirements of gravity and interconnecti…
FP64 is All You Need: Rethinking Failure Modes in Physics-Informed Neural Networks
Chenhui Xu, Dancheng Liu, Amir Nassereldine +1
Physics Informed Neural Networks (PINNs) often exhibit failure modes in which the PDE residual loss converges while the solution error stays large, a phenomenon traditionally blame…
Sub-Sequential Physics-Informed Learning with State Space Model
Chenhui Xu, Dancheng Liu, Yuting Hu +4
Physics-Informed Neural Networks (PINNs) are a kind of deep-learning-based numerical solvers for partial differential equations (PDEs). Existing PINNs often suffer from failure mod…
QuadraNet V2: Efficient and Sustainable Training of High-Order Neural Networks with Quadratic Adaptation
Chenhui Xu, Xinyao Wang, Fuxun Yu +2
Machine learning is evolving towards high-order models that necessitate pre-training on extensive datasets, a process associated with significant overheads. Traditional models, des…
Infinite-Dimensional Feature Interaction
Chenhui Xu, Fuxun Yu, Maoliang Li +4
The past neural network design has largely focused on feature representation space dimension and its capacity scaling (e.g., width, depth), but overlooked the feature interaction s…
Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble
Chenhui Xu, Fuxun Yu, Zirui Xu +2
Recent research underscores the pivotal role of the Out-of-Distribution (OOD) feature representation field scale in determining the efficacy of models in OOD detection. Consequentl…