t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning
CoRR(2022)
摘要
Inspired by the recent achievements of machine learning in diverse domains,
data-driven metamaterials design has emerged as a compelling paradigm that can
unlock the potential of multiscale architectures. The model-centric research
trend, however, lacks principled frameworks dedicated to data acquisition,
whose quality propagates into the downstream tasks. Often built by naive
space-filling design in shape descriptor space, metamaterial datasets suffer
from property distributions that are either highly imbalanced or at odds with
design tasks of interest. To this end, we present t-METASET: an
active-learning-based data acquisition framework aiming to guide both diverse
and task-aware data generation. Distinctly, we seek a solution to a commonplace
yet frequently overlooked scenario at early stages of data-driven design of
metamaterials: when a massive ( O(10^4 )) shape-only library has been prepared
with no properties evaluated. The key idea is to harness a data-driven shape
descriptor learned from generative models, fit a sparse regressor as a start-up
agent, and leverage metrics related to diversity to drive data acquisition to
areas that help designers fulfill design goals. We validate the proposed
framework in three deployment cases, which encompass general use, task-specific
use, and tailorable use. Two large-scale mechanical metamaterial datasets are
used to demonstrate the efficacy. Applicable to general image-based design
representations, t-METASET could boost future advancements in data-driven
design.
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关键词
active learning,tailoring property bias,datasets
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