On the application of Gaussian process latent force models for Bayesian identification of the Duffing system

Engineering

• Gaussians 100%
• Force Model 100%
• Nonlinearity 50%
• System Parameter 50%
• Kalman Filter 50%
• Physical System 50%
• Bayesian Framework 50%
• Nonlinear System Identification 50%
• Structural Dynamics 50%
• Nonlinear System 50%
• Posterior Distribution 50%

Keyphrases

• Bayesian Identification 100%
• Latent Force Models 100%
• Nonlinear System Identification 100%
• Physical System Models 50%

Chemical Engineering

• Nonlinear System 100%
• Learning System 50%
• Linear Systems 50%