Model selection among physics-based models
By: Hombal, V. K
.
Contributor(s): Mahadevan, S.
Publisher: New York ASME 2013Edition: Vol.135(2), Feb.Description: 1-15p.Subject(s): Mechanical EngineeringOnline resources: Click here
In:
Journal of mechanical designSummary: The optimal solution of a design optimization problem is dependent on the predictive models used to evaluate the objective and constraints. Since different models give different predictions and can yield different design decisions, when more than one model is available, the choice of model used to represent the objectives/constraints of the design becomes important. This paper addresses the problem of model selection among physics-based models during the prediction stage, which is in contrast to model selection during the calibration and validation stages, and therefore affects design under uncertainty. Model selection during calibration addresses the problem of selecting the model that is likely to provide the best generalization of the calibration data over the entire domain. Model selection during the validation stage examines the validity of a calibrated model by comparing its predictions against the validation data.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2024-0645 |
The optimal solution of a design optimization problem is dependent on the predictive models used to evaluate the objective and constraints. Since different models give different predictions and can yield different design decisions, when more than one model is available, the choice of model used to represent the objectives/constraints of the design becomes important. This paper addresses the problem of model selection among physics-based models during the prediction stage, which is in contrast to model selection during the calibration and validation stages, and therefore affects design under uncertainty. Model selection during calibration addresses the problem of selecting the model that is likely to provide the best generalization of the calibration data over the entire domain. Model selection during the validation stage examines the validity of a calibrated model by comparing its predictions against the validation data.
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