A data-driven statistical-stochastic surrogate modeling strategy for complex nonlinear non-stationary dynamics

Keyphrases

• Statistical Modeling Technique 100%
• Nonstationary Dynamics 100%
• Surrogate Model 100%
• Modeling Strategy 100%
• Stochastic Fluctuations 100%
• Numerical Examples 50%
• Modeling Approach 50%
• Energy Spectrum 50%
• Closure Model 50%
• Neural Network 50%
• Positive Definite 50%
• Modeling Framework 50%
• Loss Function 50%
• ODE System 50%
• Challenging Issues 50%
• Chaotic Dynamics 50%
• Ensemble Model 50%
• External Force 50%
• Mean Energy 50%
• Lorenz-96 Model 50%
• Fourier Modes 50%
• Effective Training 50%
• Fluctuation Dynamics 50%
• High-dimensional States 50%
• Covariance Matrix Estimation 50%
• Neural Network Architecture 50%
• Semi-parametric 50%
• Mean-variance 50%
• Parametric Modeling 50%
• Information Metric 50%
• Variance Spectrum 50%
• Modeling Paradigms 50%
• Statistical Prediction 50%
• Variance Statistics 50%
• Response Statistics 50%
• Training Procedure 50%
• Non-Markovian 50%
• Efficient Training 50%

Mathematics

• Stochastics 100%
• Surrogate Modeling 100%
• Variance 60%
• Modeling Approach 60%
• Parametric 40%
• Neural Network 40%
• Statistical Modeling 40%
• Numerical Example 20%
• State Variable 20%
• Positive Definite 20%
• Ordinary Differential Equation 20%
• Residuals 20%
• Initial Condition 20%
• Network Model 20%
• Statistical Prediction 20%
• Loss Function 20%
• Covariance Matrix Estimation 20%
• Training Procedure 20%
• Modeling Paradigm 20%