Adaptive Model Hierarchies for Multi-Query Scenarios

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Abstract

In this contribution we present an abstract framework for adaptive model hierarchies together with several instances of hierarchies for specific applications. The hierarchy is particularly useful when integrated within an outer loop, for instance an optimization iteration or a Monte Carlo estimation where for a large set of requests answers fulfilling certain criteria are required. Within the hierarchy, multiple models are combined and interact with each other pursuing the overall goal to reduce the run time in a multi-query context. To this end, models with different accuracies and effort for evaluation are used in such a way that the cheapest (and typically least accurate) models are evaluated first when a request comes in. If the result fulfills a prescribed criterion, it can be returned to the outer loop. Otherwise, the model hierarchy falls back to more costly, but at the same time more accurate, models. The cheaper models are improved by means of training data gather whenever the more accurate models are evaluated.

Speakers from the University of Münster

Kleikamp, Hendrik

Professorship of Applied Mathematics, especially Numerics (Prof. Ohlberger)