1 research outputs found
Meta-Model Framework for Surrogate-Based Parameter Estimation in Dynamical Systems
The central task in modeling complex dynamical systems is parameter
estimation. This task involves numerous evaluations of a computationally
expensive objective function. Surrogate-based optimization introduces a
computationally efficient predictive model that approximates the value of the
objective function. The standard approach involves learning a surrogate from
training examples that correspond to past evaluations of the objective
function. Current surrogate-based optimization methods use static, predefined
substitution strategies that decide when to use the surrogate and when the true
objective. We introduce a meta-model framework where the substitution strategy
is dynamically adapted to the solution space of the given optimization problem.
The meta model encapsulates the objective function, the surrogate model and the
model of the substitution strategy, as well as components for learning them.
The framework can be seamlessly coupled with an arbitrary optimization
algorithm without any modification: it replaces the objective function and
autonomously decides how to evaluate a given candidate solution. We test the
utility of the framework on three tasks of estimating parameters of real-world
models of dynamical systems. The results show that the meta model significantly
improves the efficiency of optimization, reducing the total number of
evaluations of the objective function up to an average of 77%