FAME, A Polyglot Library for Metamodeling at Runtime

Tomorrow's eternal software system will co-evolve with their context: their metamodels must adapt at runtime to ever-changing external requirements. In this paper we present FAME, a polyglot library that keeps metamodels accessible and adaptable at runtime. Special care is taken to establish causal connection between fame-classes and host-classes. As some host-languages offer limited reflection features only, not all implementations feature the same degree of causal connection. We present and discuss three scenarios: 1) full causal connection, 2) no causal connection, and 3) emulated causal connection. Of which, both Scenario 1 and 3 are suitable to deploy fully metamodel-driven applications.

Distance-based kriging relying on proxy simulations for inverse conditioning

Let us consider a large set of candidate parameter fields, such as hydraulic conductivity maps, on which we can run an accurate forward flow and transport simulation. We address the issue of rapidly identifying a subset of candidates whose response best match a reference response curve. In order to keep the number of calls to the accurate flow simulator computationally tractable, a recent distance-based approach relying on fast proxy simulations is revisited, and turned into a non-stationary kriging method where the covariance kernel is obtained by combining a classical kernel with the proxy. Once the accurate simulator has been run for an initial subset of parameter fields and a kriging metamodel has been inferred, the predictive distributions of misfits for the remaining parameter fields can be used as a guide to select candidate parameter fields in a sequential way. The proposed algorithm, Proxy-based Kriging for Sequential Inversion (ProKSI), relies on a variant of the Expected Improvement, a popular criterion for kriging-based global optimization. A statistical benchmark of ProKSI’s performances illustrates the efficiency and the robustness of the approach when using different kinds of proxies.