14 papers
Quantifying Event Impacts on Time Series via Multiscale Contrastive Learning
Yiming Sun, Shengyu Chen, Zhengzhang Chen +3
Shocks that spread through the web, such as cybersecurity breach disclosures, can abruptly disrupt financial time series and cause substantial abnormal losses. While these events a…
TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning
Yilong Dai, Yiming Sun, Yiheng Chen +4
Turbulence is a central testbed for machine learning on physical dynamics because its governing laws are known exactly. However, most existing studies remain in 2D, while 3D turbul…
Physics-Preserving Latent Compression for Zero-Shot Resolution Transfer in 3D Turbulence
Yilong Dai, Yiming Sun, Yiheng Chen +4
High-resolution turbulence modeling is essential for scientific computing, but remains constrained by the cost of direct numerical simulation and the scarcity of full-resolution da…
Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing
Yilong Dai, Shengyu Chen, Xiaowei Jia +1
Partial differential equations (PDEs) govern nearly every physical process in science and engineering, but solving them at scale remains prohibitively expensive. Generative AI has…
FlowRefiner: Flow Matching-Based Iterative Refinement for 3D Turbulent Flow Simulation
Yilong Dai, Yiming Sun, Yiheng Chen +3
Accurate autoregressive prediction of 3D turbulent flows remains challenging for neural PDE solvers, as small errors in fine-scale structures can accumulate rapidly over rollout. I…
X-MethaneWet: A Cross-scale Global Wetland Methane Emission Benchmark Dataset for Advancing Science Discovery with AI
Yiming Sun, Shuo Chen, Shengyu Chen +9
Methane (CH) is the second most powerful greenhouse gas after carbon dioxide and plays a crucial role in climate change due to its high global warming potential. Accurately mod…