OPTIMIZATION METHODS FOR PHYSICAL MODELS
According to some embodiments, system and methods are provided, comprising calculating a region of competence for a data-driven model; executing a physics-driven model when the calculated region of competence for the data-driven model falls outside of a threshold region of competence; and calibrating the physics-driven model as a function of a discrepancy between physics-driven model and actual field data when a stopping criterion has not been met. Numerous other aspects are provided.
1 . A computer-implemented method of optimizing physical simulations, comprising:
calculating a region of competence for a data-driven model;
executing a physics-driven model when the calculated region of competence for the data-driven model falls outside of a threshold region of competence; and
calibrating the physics-driven model as a function of a discrepancy between physics-driven model and actual field data when a stopping criterion has not been met.
2 . The method according to claim 1 wherein results are collected from the data-driven model when the calculated region of competence is inside the threshold region of competence.
3 . The method according to claim 1 wherein results are collected from the physics-driven model when the stopping criterion has been met.
4 . The method according to claim 1 wherein the physics-driven model is calibrated using values observed in a data-driven model to create a calibrated hybrid model.
5 . The method of claim 1 , wherein the physics-driven model is created using additional samples provided by an intelligent sampling process.
6 . The method of claim 1 , wherein calculating the region of competence for the data-driven model further comprises:
receiving one or more test sample data; and
executing a sequential optimizer model with the received one or more test sample data to compute the region of competence.
7 . The method of claim 1 , further comprising:
receiving one or more samples for evaluation by the data-driven model prior to calculating a region of competence.
8 . A system comprising:
a hybrid module;
a memory storing processor-executable steps; and
a hybrid processor coupled to the memory, and in communication with the hybrid module and operative to execute the processor-executable process steps to cause the system to:
calculate a region of competence for a data-driven model;
execute a physics-driven model when the calculated region of competence for the data-driven model falls outside of a threshold region of competence; and
calibrate the physics-driven model as a function of the discrepancy between physics-driven model and actual field data when a stopping criterion has not been met.
9 . The system of claim 8 , wherein results are collected from the data-driven model when the calculated region of competence is inside the threshold region of competence.
10 . The system of claim 8 , wherein results are collected from the physics-driven model when the stopping criterion has been met.
11 . The system of claim 8 wherein the physics-driven model is calibrated using values observed in a data-driven model to create a calibrated hybrid model.
12 . The system of claim 11 , wherein the physics-driven model is created using additional samples provided by an intelligent sampling process.
13 . The system of claim 8 , wherein calculating the region of competence for the data-driven model further comprises processor-executable process steps to cause the system to:
receive one or more test sample data; and
execute a sequential optimizer model with the received one or more test sample data to compute the region of competence.
14 . The system of claim 8 , further comprising processor-executable process steps to cause the system to:
receive one or more samples for evaluation by the data-driven model prior to calculating the region of competence.
15 . A non-transitory computer-readable medium storing program code, the program code executable by a computer system to cause the computer system to:
calculate a region of competence for a data-driven model;
execute a physics-driven model when the calculated region of competence for the data-driven model falls outside of a threshold region of competence; and
calibrate the physics-driven model as a function of the discrepancy between physics-driven model and actual field data when a stopping criterion has not been met.
16 . The medium of claim 15 , wherein results are collected from the data-driven model when the calculated region of competence is inside the threshold region of competence.
17 . The medium of claim 15 , wherein results are collected from the physics-driven model when the stopping criterion has been met.
18 . The medium of claim 15 wherein the physics-driven model is calibrated using values observed in a data-driven model to create a calibrated hybrid model.
19 . The medium of claim 18 , wherein the physics-driven model is calibrated using additional samples provided by an intelligent sampling process.
20 . The medium of claim 1 , wherein calculating the region of competence for the data-driven model further comprises processor-executable process steps to cause the system to:
receive one or more test sample data; and
execute a sequential optimizer model with the received one or more test sample data to compute the region of competence.