Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Multi-Fidelity Active Learning Method for Global Design Optimization
  Problems with Noisy Evaluations   A multi-fidelity (MF) active learning method is presented for design
optimization problems characterized by noisy evaluations of the performance
metrics. Namely, a generalized MF surrogate model is used for design-space
exploration, exploiting an arbitrary number of hierarchical fidelity levels,
i.e., performance evaluations coming from different models, solvers, or
discretizations, characterized by different accuracy. The method is intended to
accurately predict the design performance while reducing the computational
effort required by simulation-driven design (SDD) to achieve the global
optimum. The overall MF prediction is evaluated as a low-fidelity trained
surrogate corrected with the surrogates of the errors between consecutive
fidelity levels. Surrogates are based on stochastic radial basis functions
(SRBF) with least squares regression and in-the-loop optimization of
hyperparameters to deal with noisy training data. The method adaptively queries
new training data, selecting both the design points and the required fidelity
level via an active learning approach. This is based on the lower confidence
bounding method, which combines performance prediction and associated
uncertainty to select the most promising design regions. The fidelity levels
are selected considering the benefit-cost ratio associated with their use in
the training. The method's performance is assessed and discussed using four
analytical tests and three SDD problems based on computational fluid dynamics
simulations, namely the shape optimization of a NACA hydrofoil, the DTMB 5415
destroyer, and a roll-on/roll-off passenger ferry. Fidelity levels are provided
by both adaptive grid refinement and multi-grid resolution approaches. Under
the assumption of a limited budget of function evaluations, the proposed MF
method shows better performance in comparison with the model trained by
high-fidelity evaluations only.

5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
1
The categories of this paper are: cs
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci