1 citations · 4 across the 17 of their papers we have counts for
21 papers
Staying on the Attractor: Supervising Neural Surrogates of 3D Turbulence Where They Leave It
Yilong Dai, Shaswata Mitra, Raj Patel +7
Neural surrogates are trained to predict 3D turbulent flows in place of direct numerical simulation (DNS). For chaotic flows, the goal is short-term pointwise accuracy followed by…
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…
Learning PDE Solvers with Physics and Data: A Unifying View of Physics-Informed Neural Networks and Neural Operators
Yilong Dai, Shengyu Chen, Ziyi Wang +4
Partial differential equations (PDEs) are central to scientific modeling. Modern workflows increasingly rely on learning-based components to support model reuse, inference, and int…
PEST: Physics-Enhanced Swin Transformer for 3D Turbulence Simulation
Yilong Dai, Shengyu Chen, Xiaowei Jia +2
Accurate simulation of turbulent flows is fundamental to scientific and engineering applications. Direct numerical simulation (DNS) offers the highest fidelity but is computational…
BrainPuzzle: Hybrid Physics and Data-Driven Reconstruction for Transcranial Ultrasound Tomography
Shengyu Chen, Shihang Feng, Yi Luo +2
Ultrasound brain imaging remains challenging due to the large difference in sound speed between the skull and brain tissues and the difficulty of coupling large probes to the skull…
Geo-Aware Models for Stream Temperature Prediction across Different Spatial Regions and Scales
Shiyuan Luo, Runlong Yu, Shengyu Chen +4
Understanding environmental ecosystems is vital for the sustainable management of our planet. However,existing physics-based and data-driven models often fail to generalize to vary…
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…
Foundation Models for Environmental Science: A Survey of Emerging Frontiers
Runlong Yu, Shengyu Chen, Yiqun Xie +3
Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional data…
A Survey of Foundation Models for Environmental Science
Runlong Yu, Shengyu Chen, Yiqun Xie +1
Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional meth…
Modeling Continuous Spatial-temporal Dynamics of Turbulent Flow with Test-time Refinement
Shengyu Chen, Peyman Givi, Can Zheng +1
The precise simulation of turbulent flows holds immense significance across various scientific and engineering domains, including climate science, freshwater science, and energy-ef…
Physics-enhanced Neural Operator for Simulating Turbulent Transport
Shengyu Chen, Peyman Givi, Can Zheng +1
The precise simulation of turbulent flows is of immense importance in a variety of scientific and engineering fields, including climate science, freshwater science, and the develop…
FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems
Shiyuan Luo, Juntong Ni, Shengyu Chen +6
Modeling environmental ecosystems is critical for the sustainability of our planet, but is extremely challenging due to the complex underlying processes driven by interactions amon…
HOSSnet: an Efficient Physics-Guided Neural Network for Simulating Crack Propagation
Shengyu Chen, Shihang Feng, Yao Huang +4
Hybrid Optimization Software Suite (HOSS), which is a combined finite-discrete element method (FDEM), is one of the advanced approaches to simulating high-fidelity fracture and fra…
Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement
Shengyu Chen, Tianshu Bao, Peyman Givi +2
Simulating turbulence is critical for many societally important applications in aerospace engineering, environmental science, the energy industry, and biomedicine. Large eddy simul…
Modeling Reservoir Release Using Pseudo-Prospective Learning and Physical Simulations to Predict Water Temperature
Xiaowei Jia, Shengyu Chen, Yiqun Xie +4
This paper proposes a new data-driven method for predicting water temperature in stream networks with reservoirs. The water flows released from reservoirs greatly affect the water…
Heterogeneous Stream-reservoir Graph Networks with Data Assimilation
Shengyu Chen, Alison Appling, Samantha Oliver +5
Accurate prediction of water temperature in streams is critical for monitoring and understanding biogeochemical and ecological processes in streams. Stream temperature is affected…
Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks
Shengyu Chen, Shervin Sammak, Peyman Givi +2
Direct numerical simulation (DNS) of turbulent flows is computationally expensive and cannot be applied to flows with large Reynolds numbers. Large eddy simulation (LES) is an alte…