Metamodel-assisted analysis of an integrated model composition: an example using linked surface water-groundwater models

Integrated modelling is a promising approach to simulate processes operating within complex environmental systems. It is possible, however, that this integration may lead to computationally expensive compositions. In order to retain the process fidelity without loss of accuracy, the use of Kriging metamodels is proposed to perform Monte Carlo simulation and sensitivity analysis, in lieu of compositions developed using the model linking standard OpenMI. Results from the Monte Carlo simulation showed that the metamodels were in a good agreement with the original responses. However, metamodels provided a less accurate approximation of the original output distribution for the composition which involved a stronger non-linear behaviour. The fast runtimes of the metamodels allowed for increased computational budgets leading to an accurate screening of the important parameters for an Elementary Effects Test. Overall, Kriging metamodels provided significant computational savings without compromising the quality of the outcomes, even using small training data sets

Surrogate-based pumping optimization of coastal aquifers under limited computational budgets

The computationally expensive variable density and salt transport numerical models hinder the implementation of simulation-optimization routines for coastal aquifer management. To reduce the computational cost, surrogate models have been utilized in pumping optimization of coastal aquifers. However, it has not been previously addressed whether surrogate modelling is effective given a limited number of numerical simulations with the seawater intrusion model. To that end, two surrogate-based optimization (SBO) frameworks are employed and compared against the direct optimization approach, under restricted computational budgets. The first, a surrogate-assisted algorithm, employs a strategy which aims at a fast local improvement of the surrogate model around optimal values. The other, balances global and local improvement of the surrogate model and is applied for the first time in coastal aquifer management. The performance of the algorithms is investigated for optimization problems of moderate and large dimensionalities. The statistical analysis indicates that for the specified computational budgets, the sample means of the SBO methods are statistically significantly better than those of the direct optimization. Additionally, the selection of cubic radial basis functions as surrogate models, enables the construction of very fast approximations for problems with up to 40 decision variables and 40 constraint functions

An adaptive multi-fidelity optimization framework based on co-Kriging surrogate models and stochastic sampling with application to coastal aquifer management

Surrogate modelling has been used successfully to alleviate the computational burden that results from high-fidelity numerical models of seawater intrusion in simulation-optimization routines. Nevertheless, little attention has been given to multi-fidelity modelling methods to address cases where only limited runs with computationally expensive seawater intrusion models are considered affordable imposing a limiting factor for single-fidelity surrogate-based optimization as well. In this work, a new adaptive multi-fidelity optimization framework is proposed based on co-Kriging surrogate models considering two model fidelities of seawater intrusion. The methodology is tailored to the needs of solving pumping optimization problems with computationally expensive constraint functions and utilizes only small high-fidelity training datasets. Results from both hypothetical and real-world optimization problems demonstrate the efficiency and practicality of the proposed framework to provide a steep improvement of the objective function while it outperforms a comprehensive single-fidelity surrogate-based optimization method. The method can also be used to locate optimal solutions in the region of the global optimum when larger high-fidelity training datasets are available

Designing minimal effective normative systems with the help of lightweight formal methods

Normative systems (i.e., a set of rules) are an important approach to achieving effective coordination among (often an arbitrary number of) agents in multiagent systems. A normative system should be effective in ensuring the satisfaction of a desirable system property, and minimal (i.e., not containing norms that unnecessarily over-constrain the behaviors of agents). Designing or even automatically synthesizing minimal effective normative systems is highly non-trivial. Previous attempts on synthesizing such systems through simulations often fail to generate normative systems which are both minimal and effective. In this work, we propose a framework that facilitates designing of minimal effective normative systems using lightweight formal methods. Given a minimal effective normative system which coordinates many agents must be minimal and effective for a small number of agents, we start with automatically synthesizing one such system with a few agents. We then increase the number of agents so as to check whether the same design remains minimal and effective. If it is, we manually establish an induction proof so as to lift the design to an arbitrary number of agents