Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Bayesian Search for the Feasible Space Under Computationally
  Expensive Constraints   We are often interested in identifying the feasible subset of a decision
space under multiple constraints to permit effective design exploration. If
determining feasibility required computationally expensive simulations, the
cost of exploration would be prohibitive. Bayesian search is data-efficient for
such problems: starting from a small dataset, the central concept is to use
Bayesian models of constraints with an acquisition function to locate promising
solutions that may improve predictions of feasibility when the dataset is
augmented. At the end of this sequential active learning approach with a
limited number of expensive evaluations, the models can accurately predict the
feasibility of any solution obviating the need for full simulations. In this
paper, we propose a novel acquisition function that combines the probability
that a solution lies at the boundary between feasible and infeasible spaces
(representing exploitation) and the entropy in predictions (representing
exploration). Experiments confirmed the efficacy of the proposed function.

34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph