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Surrogate model

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2021. 1. 29. · Surrogate-Based Optimization¶. Surrogate-based optimization represents a class of optimization methodologies that make use of surrogate modeling techniques to quickly find the local or global optima. It provides us a novel optimization framework in which the conventional optimization algorithms, e.g. gradient-based or evolutionary algorithms are used
2.2.2 Function Approximation Surrogates. Functional models, also known as response surface approximation or data-driven models, utilize simulation and/or measurement data to mimic the behavior of the system within a defined region of the search space [142]. RSA models are generic in a sense that they are applicable to variety of problems.
The proposed surrogate model relies on a multi-fidelity (MF) deep neural network (DNN), mapping the damage and operational parameters onto sensor recordings. The MF-DNN is shown to effectively ...
It offers model agnostic tools like model performance, variable importance, global surrogate models, ICE profiles, partial Global surrogate models. As suggested in (Molnar 2019), one way to explain...
The surrogate model is based on deep convolutional and recurrent neural network architectures, specifically a residual U-Net and a convolutional long short term memory recurrent network. Training samples entail global pressure and saturation maps, at a series of time steps, generated by simulating oil-water flow in many (1500 in our case ...