-
Chaopeng Shen
Pennsylvania State University Main CampusMeeting roles in:
Improving Landslide Susceptibility Prediction with Terrain Patterns, High-Resolution Data, and Ensemble Models
Differentiable high-resolution hydrologic and water quality simulations transform global hydrologic research
Distributed Differentiable Routing on the CONUS Hydrofabric
When Stream Temperature Meets Streamflow: A Scalable Approach to Modeling Climate-Sensitive Groundwater Recharge
Differentiable Downscaling Model for GCMs Precipitation
Toward Generalizable and Interpretable Water Quality Modeling with AI-Augmented HSPF
How reliable are global river discharge simulations?
A Pretrained Foundation AI Model Reveals Global Landscape Dynamics and Human-driven Ecosystem Deviations
A Differentiable Ecosystem Modeling Approach: A Machine Learning - Physics Hybrid for Simulating Plant Water and Carbon Fluxes
Frontier AI Models Transforming Water Science I Oral
Modeling Freshwater Inflows to Estuaries under Climate Change: A Physically-Based Approach
Uncertainty Estimation and Explanation for Convective Initiation Nowcasting Using Bayesian Deep Learning
Improved Multi-Domain Environmental Forecasting Through Landscape-Based AI Modeling
Probabilistic Diffusion Models Advance Extreme Flood Forecasting
Frontier AI Models Transforming Water Science II Poster
An Assessment of the Value of SWOT River and Lake Data for Hydrologic Modeling Using Physics-embedded Learning
A Multiscale Machine Learning Framework for Suspended Sediment Concentration Prediction across the Contiguous United States
Unifying Advances in Differentiable Modeling for High-Performance Operational Streamflow Forecasting
Machine Learning, Physics, and Generative AI for Hydrologic and River Modeling I Poster
From Predictions to Patterns with AI: A Differentiable SPARROW Framework for Improved Water Quality Prediction and Attribution
Modeling Photosynthesis and Plant Hydraulics Using a Differentiable Physics-Informed Machine Learning Framework
DMFS: Differentiable Modeling for Forzen Soil Thermodynamic Characteristics
Accelerating Continental-Scale Differentiable Modeling with Sensitivity-Constrained AI Surrogates
Accurate and Efficient Hourly Streamflow Downscaling or Prediction with Diffusion Model
Machine Learning, Physics, and Generative AI for Hydrologic and River Modeling II Oral
Structural Bias Should Be Addressed Before Effective Parameter Learning — Insights from SMAP Soil Moisture Simulations Using Differentiable Process-Based Models
Differentiable Bias Adjustment Model for GCMs Precipitation
Global Reach Scale Hydrology: Progress and Challenges
Distinct Hydrologic Response Patterns and Trends Worldwide Revealed by Physics-embedded Learning
Unexpectedly Strong Landscape Interconnections Captured by Earth Foundation AI for Land
Global High-Resolution Hydrologic Simulation to Enhance Predictions in Ungauged Regions with Hybrid Differentiable Models
The AI-surpassing Physics-embedded AI model for Capturing Unseen Extreme Flood Events and Climate Change Impact Assessment
Differentiable Physics‐Informed Machine Learning Enhances High-resolution Hydrologic Modeling
Sensitivity-Constrained Neural Operators for Forward and Inverse Problems in High-Dimensional PDEs
Enter Note
Go to previous page in this tab
Author/Chair
