Computer Model Predictions of Ocean Basin Reverberation for Large Underwater Explosions

Author(s):  
Jean A. Goertner
1992 ◽  
Vol 11 (3) ◽  
pp. 427-436 ◽  
Author(s):  
Thomas C. Mueller ◽  
Parshall B. Bush ◽  
Philip A. Banks ◽  
Ronald E. Jones

Author(s):  
James C. Cavendish ◽  
John A. Cafeo

We view the most important question in evaluation of a computer model to be: Does the computer model provide predictions that are accurate enough for the intended use of the model? The purpose of this presentation is to discuss a systematic six-step model validation process intended to help answer that question. This will be done by presenting a Bayesian statistical strategy for developing error bounds on model predictions with the interpretation that there is a specified confidence (e.g. 80%) that the corresponding true process value will lie within the range of these error bounds. Although seldom done in practice, such error bounds and confidence estimates should be provided whenever model predictions are made. A Caveat: The process of model validation is inherently a hard statistical problem. The statistical problem is so hard that one rarely sees model validation approaches that actually produce error bounds and confidence estimates on computer model predictions. The intent of this presentation is essentially to provide a ‘proof of concept’, that it is possible to provide such bounds and estimates for predictions of computer models, while taking into account all of the uncertainties present in the problem. However, the computations required in the methodology we propose can be intensive, especially when there are large numbers of model inputs, large numbers of unknown parameters, or a large amount of data (model-run or field). The test bed application we consider in this presentation (a resistance spot weld model) is relatively modest in these dimensions. Finally, we call the reader’s attention to the reference, Bayarri et. al. (2002). This reference provides a down-loadable PDF file that contains a technical report presenting all of the technical details associated with our proposed validation strategy as well as practical application of the strategy to the spot weld model described in this presentation as well as to an automobile crash model.


Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 3848-3848
Author(s):  
Vladimir Vainstein ◽  
Yuval Ginosar ◽  
Meir Shoham ◽  
Anton Ianovski ◽  
Alexander Rabinovich ◽  
...  

Abstract Neutropenia is a dose-limiting toxicity in dose-intensified chemotherapy regimens. Yet to be determined are the lower limit of inter-dosing interval of chemotherapy and the optimal schedules of GCSF support. In the absence of better tools, the most promising schedules to be tested in clinical trials are selected by trial and error. In order to provide a scientific tool for treatment selection, a physiologically-based, computer-implemented, mathematical model of human granulopoiesis was recently developed (Vainstein et al, J Theor Biol, 2005). The aim of the current study is to validate the model clinically and to use it for suggesting an improved doxorubicin monotherapy schedule, with GCSF support. First, the model was validated by showing its accurate predictions of neutropenia dynamics in patients treated by doxorubicin 75mg/m2 q14d, with GCSF support (clinical data from Bronchud et al, Br J Cancer, 1989). To validate the model ability to predict treatment outcomes for individual patients, it was simulated in conjunction with base-line blood counts and treatment schedules of ten breast cancer patients, who received different doxorubicin monotherapy protocols (20–30mg/m2 q7d, 60–75mg/m2 q21d). Model predictions for each patient were then compared with the patient’s neutrophil profile. Results showed that the model accurately predicted doxorubicin-induced neutropenia course in all patients examined (see example in Figure 1). To identify a new intensified regimen, which minimizes myelotoxicity, simulations were performed of different doxorubicin+GCSF treatment schedules. Results suggest that 4 days is the optimal gap before starting GCSF support, following administration of doxorubicin 75mg/m2 q14d. No grade 3/4 neutropenia is expected under such a regimen, as compared to 3–4 days neutropenia when the gap before starting GCSF support was 1 day (Bronchud et al, 1989). It is further predicted that under the suggested schedule of GCSF support, doxorubicin monotherapy can be intensified, either to120mg/m2 q14d, by dose-escalation alone, or to 90mg/m2 q10d, by combining dose-escalation and increased dose-density. Such intensification is expected to result in 1–2 days of grade 3/4 neutropenia, as compared to 3–4 days long neutropenia in the doxorubicin schedule used by Bronchud et al., which has only 62% of the proposed doxorubicin dose intensity. Further research is warranted for clinically validating the superiority of the suggested treatment schedules. These results suggest that if the involved cellular dynamics are precisely calculated, doxorubicin schedule can be further intensified, accompanied by GCSF support, with no significant increase in myelotoxicity. Showing precision in predicting individual patient’s counts, the computer model of human granulopoiesis can be used for treatment personalization. Figure Figure


1995 ◽  
Vol 398 ◽  
Author(s):  
K. F. Kelton

ABSTRACTA realistic computer model for polymorphic crystallization under isothermal and nonisothermal conditions, which takes proper account of time-dependent nucleation behavior and cluster-size-dependent growth, is presented. A new correction to the standard Johnson-Mehl-Avrami-Kolmogorov (JMAK) statistical analysis that takes account of finite sample size is incorporated to simulate data taken from fine particles and nano-structured materials. Model predictions compare well with experimental data obtained from calorimetric studies of the polymorphic crystallization of lithium disilicate glass. The computer model is employed to evaluate commonly used methods of analysis for calorimetric data and to suggest new approaches for extracting kinetic parameters.


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