500 citations · 2k across the 65 of their papers we have counts for
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Transolver-: Joint Spectral-Physical Subspace Modeling for Neural PDE Solving
Haonan Shangguan, Hang Zhou, Haixu Wu +3
Neural solvers offer efficient surrogates for numerical simulation of partial differential equations (PDEs). For time-dependent problems, strong one-step accuracy does not necessar…
NowcastDiT: Diffusion Transformers are Effective Precipitation Nowcasters
Haoran Xu, Xingzhuo Guo, Yuchen Zhang +3
Precipitation nowcasting demands accurate short-term forecasts under strong spatiotemporal variability. Diffusion models are well suited to modeling complex precipitation distribut…
Harnessing AI for Inverse Partial Differential Equation Problems: Past, Present, and Prospects
Zhentao Tan, Yuze Hao, Boyi Zou +3
Solving inverse partial differential equation (PDE) problems is a fundamental topic in scientific research due to its broad significance across a wide range of real-world applicati…
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…
Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting
Yunzhong Qiu, Binzhu Li, Hao Wei +5
Electricity market prices exhibit extreme volatility, nonlinearity, and non-stationarity, making accurate forecasting a significant challenge. While cutting-edge time series founda…
BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization
Zhongyi Pei, Zhiyao Cen, Yipeng Huang +4
Hyperparameter optimization (HPO) is known to be costly in deep learning, especially when leveraging automated approaches. Most of the existing automated HPO methods are accuracy-b…
DiTS: Multimodal Diffusion Transformers Are Time Series Forecasters
Haoran Zhang, Haixuan Liu, Yong Liu +4
While generative modeling facilitates probabilistic time series forecasting, incorporating heterogeneous exogenous information remains challenging. Diffusion Transformers (DiT) pro…
GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training
Haixu Wu, Minghao Guo, Zongyi Li +4
Neural simulators promise efficient surrogates for physics simulation, but scaling them is bottlenecked by the prohibitive cost of generating high-fidelity training data. Pre-train…
It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks
Zhongzheng Qiao, Sheng Pan, Anni Wang +7
Time series foundation models (TSFMs) are revolutionizing the forecasting landscape from specific dataset modeling to generalizable task evaluation. However, we contend that existi…
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…
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…