11 papers
Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries
Hang Zhou, Haixu Wu, Haonan Shangguan +4
Deep learning has emerged as a transformative tool for the neural surrogate modeling of partial differential equations (PDEs), known as neural PDE solvers. However, scaling these s…
Brep2Shape: Boundary and Shape Representation Alignment via Self-Supervised Transformers
Yuanxu Sun, Yuezhou Ma, Haixu Wu +4
Boundary representation (B-rep) is the industry standard for computer-aided design (CAD). While deep learning shows promise in processing B-rep models, existing methods suffer from…
TimeVista: Exploring and Exploiting Vision-Language Models as Judges for Time Series Forecasting
Zhi Chen, Yuxuan Wang, Jialong Wu +5
High-quality time series forecasting is pivotal for real-world decision-making. However, traditional point-wise metrics often fail to reveal complex temporal patterns and align poo…
PhySense: Sensor Placement Optimization for Accurate Physics Sensing
Yuezhou Ma, Haixu Wu, Hang Zhou +3
Physics sensing plays a central role in many scientific and engineering domains, which inherently involves two coupled tasks: reconstructing dense physical fields from sparse obser…
Exploring Accuracy Law for Deep Time Series Forecasters: An Empirical Study
Yuxuan Wang, Haixu Wu, Yuezhou Ma +8
Deep time series forecasting has emerged as a rapidly growing field in recent years. Despite the exponential growth of community interests, progress on standard benchmarks is often…
Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling
Yong Liu, Xingjian Su, Shiyu Wang +7
We introduce Timer-S1, a strong Mixture-of-Experts (MoE) time series foundation model with 8.3B total parameters, 0.75B activated parameters for each token, and a context length of…