  Adaptive Computing is an application-agnostic outer loop framework to
strategically deploy simulations and experiments to guide decision making for
scale-up analysis. Resources are allocated over successive batches, which makes
the allocation adaptive to some objective such as optimization or model
training. The framework enables the characterization and management of
uncertainties associated with predictive models of complex systems when
scale-up questions lead to significant model extrapolation. A key feature of
this framework is the ability to explicitly utilize user-specified uncertainty
priors, which we call model-specific local trust estimates, that are provided
directly together with the problem specification and exploited in adaptive
sampling strategies. A multi-fidelity model hierarchy is supported to allow
trade-offs in accuracy and data acquisition cost while exploring the search
space given a specified budget of potentially distributed, heterogeneous
resources. We discuss application of this framework to problems in the
renewable energy space, including biofuels production, material synthesis,
perovskite crystal growth, and building electrical loads.
