3,816 research outputs found
Topology Optimization via Machine Learning and Deep Learning: A Review
Topology optimization (TO) is a method of deriving an optimal design that
satisfies a given load and boundary conditions within a design domain. This
method enables effective design without initial design, but has been limited in
use due to high computational costs. At the same time, machine learning (ML)
methodology including deep learning has made great progress in the 21st
century, and accordingly, many studies have been conducted to enable effective
and rapid optimization by applying ML to TO. Therefore, this study reviews and
analyzes previous research on ML-based TO (MLTO). Two different perspectives of
MLTO are used to review studies: (1) TO and (2) ML perspectives. The TO
perspective addresses "why" to use ML for TO, while the ML perspective
addresses "how" to apply ML to TO. In addition, the limitations of current MLTO
research and future research directions are examined
Discriminative Density-ratio Estimation
The covariate shift is a challenging problem in supervised learning that
results from the discrepancy between the training and test distributions. An
effective approach which recently drew a considerable attention in the research
community is to reweight the training samples to minimize that discrepancy. In
specific, many methods are based on developing Density-ratio (DR) estimation
techniques that apply to both regression and classification problems. Although
these methods work well for regression problems, their performance on
classification problems is not satisfactory. This is due to a key observation
that these methods focus on matching the sample marginal distributions without
paying attention to preserving the separation between classes in the reweighted
space. In this paper, we propose a novel method for Discriminative
Density-ratio (DDR) estimation that addresses the aforementioned problem and
aims at estimating the density-ratio of joint distributions in a class-wise
manner. The proposed algorithm is an iterative procedure that alternates
between estimating the class information for the test data and estimating new
density ratio for each class. To incorporate the estimated class information of
the test data, a soft matching technique is proposed. In addition, we employ an
effective criterion which adopts mutual information as an indicator to stop the
iterative procedure while resulting in a decision boundary that lies in a
sparse region. Experiments on synthetic and benchmark datasets demonstrate the
superiority of the proposed method in terms of both accuracy and robustness
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