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Dan Lu, Ph.D.
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Research Highlights
An uncertainty quantification method for machine learning models
PI3NN-LSTM for robust time series prediction
Real-time forecast for geological carbon storage
Invertible neural networks for fast model simulation and calibration
Interpretable LSTM model for NEE prediction
Bayesian neural networks for ensemble climate model predictions
Surrogate methods for large-scale Earth system models
Bayesian method for AI model UQ
A learning-based inversion-free framework
Publications
Talks
Projects
Softwares
Services
Dan Lu, Ph.D.
Home
Research Highlights
An uncertainty quantification method for machine learning models
PI3NN-LSTM for robust time series prediction
Real-time forecast for geological carbon storage
Invertible neural networks for fast model simulation and calibration
Interpretable LSTM model for NEE prediction
Bayesian neural networks for ensemble climate model predictions
Surrogate methods for large-scale Earth system models
Bayesian method for AI model UQ
A learning-based inversion-free framework
Publications
Talks
Projects
Softwares
Services
More
Home
Research Highlights
An uncertainty quantification method for machine learning models
PI3NN-LSTM for robust time series prediction
Real-time forecast for geological carbon storage
Invertible neural networks for fast model simulation and calibration
Interpretable LSTM model for NEE prediction
Bayesian neural networks for ensemble climate model predictions
Surrogate methods for large-scale Earth system models
Bayesian method for AI model UQ
A learning-based inversion-free framework
Publications
Talks
Projects
Softwares
Services
Research Highlights
A uncertainty quantification method for machine learning models
PI3NN-LSTM: a data-shift-aware machine learning model for robust time-series prediction
An interpretable LSTM model for advancing carbon flux prediction
Invertible neural networks for fast model simulation and calibration
Real-time forecast of geological carbon storage
Bayesian neural networks for ensemble climate model predictions
Surrogate methods for
large-scale Earth system models
Bayesian method for AI model uncertainty quantification
A
learning
-based inversion-free framework for complex model prediction
A Scalable Evolution Strategy for High-Dimensional Blackbox Optimization
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