It is concluded that tools for the efficient development and testing of physically based dynamic models are of central importance for a model-based concurrent approach to the design of integrated engineering systems.
Tools for the efficient development and testing of physically based dynamic models are of central importance for this and include forward and inverse simulation, parameter sensitivity analysis, system identification, parameter estimation, optimisation and partial system testing through hardware-in-the-loop simulation.
We applied our GP based dynamic model to the same data as obtained from ArrayExpress (European Bioinformatics Institute).
For each experience, a difference equation based dynamic model has been established.
In recent years, several physics-based dynamic models for landslides have been developed based on the constitutive law of fluid mechanics (Pitman et al. 2003; McDougall and Hungr 2004, 2005; Goren and Aharonov 2007; Goren and Aharonov 2009; George and Iverson 2011; Luna and Remaitre 2012).
A general protocol is presented for developing a detailed network-based dynamic model for S. oneidensis based on the Lumped Hybrid Cybernetic Model (L-HCM) framework.
Differential algebraic equation based dynamic sub-models are developed for key components, including compressors, internal heat exchangers, a storage chamber and turbines.
In this paper, initially we propose a robust feedforward neural network (FFNN) based dynamic weighted combination model (PFFNNDWCM) for software reliability prediction.
In this paper, a physics-based dynamic model based on Savage–Hutter theory for landslide has been presented.
The problem is examined based on the full nonlinear coupled dynamic models for collinear and general triangular configurations.
In previous work, a calibration procedure for conceptual models based on the estimation of black-box dynamic models for the RRR has been proposed, thereby shifting the problem from the actual tuning of conceptual models to the identification of black-boxmodels on the basis of rainfall and runoff measurements.
