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Data-driven and physics-based modeling

Data-driven and physics-based modeling

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Utilization of computer simulations has become ubiquitous in contemporary engineering design. Accurate simulations can provide a reliable assessment of components and devices, thereby replace the need for prototyping, reduce the cost of the design cycle, as well as its cost. At the same time, the computational cost of computer simulations (e.g., full -wave electromagnetic (EM) analysis in microwave or antenna engineering) maybe considerable or even unmanageable, particularly for complex structures. This is usually not a problem for design verification or even simple simulation -driven design procedures based on parameter sweeping; however, it becomes problematic for procedures that require numerous analyses, such as parametric optimization, statistical analysis, or tolerance -aware design.

Chapter Contents:

  • 4.1 Model classification
  • 4.2 Modeling flow
  • 4.3 Design of experiments
  • 4.4 Data-driven models
  • 4.4.1 Polynomial regression
  • 4.4.2 Radial basis function interpolation
  • 4.4.3 Kriging
  • 4.4.4 Support vector regression
  • 4.4.5 Neural networks
  • 4.4.6 Other methods
  • 4.5 Physics-based models
  • 4.5.1 Variable fidelity models
  • 4.5.2 Space mapping
  • 4.5.3 Response correction methods
  • 4.5.4 Feature-based modeling
  • 4.6 Model selection and validation
  • 4.7 Summary
  • Acknowledgments
  • References

Inspec keywords: computer simulation; optimisation; statistical analysis; CAD

Other keywords: physics-based modeling; data-driven modeling; tolerance -aware design; contemporary engineering design; computer simulations; parameter sweeping; design verification; simulation -driven design procedures; parametric optimization; statistical analysis

Subjects: Optimisation techniques; Other topics in statistics; Simulation techniques; Engineering computing

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