Buoyancy Regulation and Vertical Migration of Trichodesmium: a Computer-Model Prediction

1992 ◽  
pp. 239-248 ◽  
Author(s):  
Jacco Kromkamp ◽  
Anthony E. Walsby
Keyword(s):  
Computer Model ◽  
Model Prediction ◽  
Vertical Migration

A computer model of buoyancy and vertical migration in cyanobacteria

1990 ◽  
Vol 12 (1) ◽  
pp. 161-183 ◽  
Author(s):  
Jacco Kromkamp ◽  
Anthony E. Walsby
Keyword(s):  
Computer Model ◽  
Vertical Migration

Computer model prediction of left ventricular hypertrophy based on the concept of a double loaded ventricle

1995 ◽  
Vol 8 (3) ◽  
pp. 383 ◽  
Author(s):  
Arzu Ilercil ◽  
Ying Zhu ◽  
Jiajia Wu ◽  
John K-J. Li ◽  
Myung-Ho Lee ◽  
...  
Keyword(s):  
Left Ventricular Hypertrophy ◽  
Computer Model ◽  
Model Prediction ◽  
Ventricular Hypertrophy ◽  
Left Ventricular

Some Metrics and a Bayesian Procedure for Validating Predictive Models in Engineering Design

2006 ◽  
Author(s):  
Wei Chen ◽  
Ying Xiong ◽  
Kwok-Leung Tsui ◽  
Shuchun Wang
Keyword(s):  
Engineering Design ◽  
Model Validation ◽  
Computer Model ◽  
Bayesian Approach ◽  
Model Prediction ◽  
Predictive Models ◽  
Design Decision ◽  
Validation Metrics ◽  
Model Based ◽  
Physical Experiments

Even though model-based simulations are widely used in engineering design, it remains a challenge to validate models and assess the risks and uncertainties associated with the use of predictive models for design decision making. In most of the existing work, model validation is viewed as verifying the model accuracy, measured by the agreement between computational and experimental results. However, from the design perspective, a good model is considered as the one that can provide the discrimination (good resolution) between design candidates. In this work, a Bayesian approach is presented to assess the uncertainty in model prediction by combining data from both physical experiments and the computer model. Based on the uncertainty quantification of model prediction, some design-oriented model validation metrics are further developed to guide designers for achieving high confidence of using predictive models in making a specific design decision. We demonstrate that the Bayesian approach provides a flexible framework for drawing inferences for predictions in the intended but may be untested design domain, where design settings of physical experiments and the computer model may or may not overlap. The implications of the proposed validation metrics are studied, and their potential roles in a model validation procedure are highlighted.

Application of Prometheus III, Simulation of Human Crash Dynamics for Energy Absorption Studies

1982 ◽  
Vol 26 (11) ◽  
pp. 979-983
Author(s):  
David W. Twigg ◽  
Donald L. Parks
Keyword(s):  
Energy Absorption ◽  
Test Data ◽  
Computer Model ◽  
Model Prediction ◽  
Trade Off ◽  
Absorption Studies ◽  
Energy Absorbing ◽  
Prediction Capability ◽  
Actual Test

Refinement of the PROMETHEUS III computer model of human crash dynamics to simulate the performance of energy absorbing seats is described. Calibration against test data for a plastically deforming seat is presented. Some of the characteristics that are concluded to have been important to achieving the calibration are discussed. General techniques for using the model to produce engineering trade-off data are also discussed, including exploration of alternate seating concepts. Emphasis is given to the critical need to verify model prediction capability based on calibrations using actual test data as a prerequisite to engineering acceptance and usage.

1593: Computer Model to Predict Patency Outcome after Vasoepididymostomy

2004 ◽  
Vol 171 (4S) ◽  
pp. 420-420
Author(s):  
Sijo J. Parekattil ◽  
Paul Shin ◽  
Anthony J. Thomas ◽  
Ashok Agarwal
Keyword(s):  
Computer Model

Interpreting Open- and Closed-Loop Transfer Relations Between Cardiorespiratory Parameters: Lessons Learned from a Computer Model of Beat-to-Beat Cardiovascular Regulation

1997 ◽  
Vol 36 (04/05) ◽  
pp. 237-240
Author(s):  
P. Hammer ◽  
D. Litvack ◽  
J. P. Saul
Keyword(s):  
Computer Model ◽  
Closed Loop ◽  
Computer Models ◽  
Cardiovascular Regulation ◽  
Cardiovascular Control ◽  
Lessons Learned ◽  
Multiple Systems ◽  
Response Characteristics ◽  
Similarities And Differences

Abstract:A computer model of cardiovascular control has been developed based on the response characteristics of cardiovascular control components derived from experiments in animals and humans. Results from the model were compared to those obtained experimentally in humans, and the similarities and differences were used to identify both the strengths and inadequacies of the concepts used to form the model. Findings were confirmatory of some concepts but contrary to some which are firmly held in the literature, indicating that understanding the complexity of cardiovascular control probably requires a combination of experiments and computer models which integrate multiple systems and allow for determination of sufficiency and necessity.

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