GaussED: A Python Package for Sequential Experimental Design
Abstract: Sequential algorithms are popular for experimental design, enabling emulation, optimisation and inference to be efficiently performed. For most of these applications bespoke software has been developed, but the approach is general and many of the actual computations performed in such software are identical. Motivated by the diverse problems that can in principle be solved with common code, this paper presents GaussED, a high-level syntax coupled to a powerful experimental design engine in Python, which together automate sequential experimental design for approximating a (possibly nonlinear) quantity of interest in Gaussian processes models. Using a handful of commands, GaussED can be used to: solve linear partial differential equations, perform tomographic reconstruction from integral data, implement Bayesian optimisation with gradient data, and emulate a complex computer model.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: We have revised the manuscript based on the feedback from the 3 expert reviewers.
Assigned Action Editor: ~Roman_Garnett1
Submission Number: 2304
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