MetaModeling
There are 2 different ways to create the model codes: physicsmodel and datamodel. Its advantages and disadvantages of which complement each other. The combination of both methods brings advances and undreamtof possibilities for modeling real objects. OptiY Software offers this approach called metamodeling which is the combiation of surrogate model and physicsinformed machine learning. The new way of modeling is any mix of some imperfect data and some imperfect physical components getting accurate metamodel for realtime computing and it is therefore an indispensable tool for simulation engineers.
PhysicsModel  DataModel 


Physical model is used as object for data generation. Numerous commercial simulation systems can be coupled with OptiY. Using of statistical design of experiments, the entire parameter space is stretched and calculated. With these data obtained, accurate metamodel can be mapped using machine learning.
If the data does not have the required quantity to generate accurate metamodels, additional data points can be generated with the help of coupled physics model. Using of adaptive design of experiments from OptiY, which are based on existing data, new targeted data points can be very efficiently proposed and calculated by the physics model. Measurement data can then be mapped together with newly additional simulation data in sufficient quantities to accurate metamodel.
Case Studies
 Datadriven Modeling and Simulation of a double Spring Mass Damper System
 AutoExtraction of Modelica Code from Finite Element Analysis or Measurement Data
 Magnetic Switch Mechanism for Circuit Breakers