Applied Sciences,
Год журнала:
2024,
Номер
14(24), С. 11549 - 11549
Опубликована: Дек. 11, 2024
This
paper
focuses
on
presenting
an
intelligent
model
that
can
generate
the
desired
geometry
of
a
unit
cell
metasurface
for
given
resonant
frequency
at
which
we
expect
structure
to
work.
The
consists
use
multilayer
perceptron
and
filters,
represent
output
as
6
×
matrix
stored
in
binary
state.
value
0
denotes
dielectric
substrate
is
built,
1
blocks
conducting
parts
metasurface.
proposed
was
tested
using
simulation
data
from
Comsol
Multiphysics
environment.
test
confirmed
effectiveness
model,
it
possible
develop
apply
larger
other
datasets.
Advanced Engineering Materials,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 29, 2025
Metamaterials,
engineered
to
exhibit
unique
properties,
not
found
in
natural
materials,
are
a
key
focus
of
modern
scientific
research.
Acoustic
metamaterials
designed
manipulate
or
attenuate
acoustic
waves.
Early
designs
show
promising
results
attenuating
sound
waves
medium
and
high‐frequency
ranges
but
lack
effectiveness
for
low
frequencies.
In
recent
years,
there
has
been
shift
toward
the
research
passive
metamaterials,
frequencies,
with
trend
additive
manufacturing
ease
fabrication.
Over
45
design
theories
have
reviewed,
along
112
low‐frequency
last
5
years.
This
comprehensive
review
ensures
validity
reliability
present
findings
equips
knowledge
select
most
appropriate
theory,
metamaterial
type,
testing
standards
analyzing
metamaterial.
The
article
also
discusses
computational
methods
process
compares
various
existing
their
applications
areas
like
environmental
noise
reduction,
isolation,
other
health‐related
applications.
Finally,
it
reviews
experimental
verification
metamaterials.
aims
steer
future
course
science
by
integrating
wide
range
research,
instilling
confidence
findings.
Nanophotonics,
Год журнала:
2025,
Номер
14(4), С. 429 - 447
Опубликована: Фев. 3, 2025
Abstract
Empowering
nanophotonic
devices
via
artificial
intelligence
(AI)
has
revolutionized
both
scientific
research
methodologies
and
engineering
practices,
addressing
critical
challenges
in
the
design
optimization
of
complex
systems.
Traditional
methods
for
developing
are
often
constrained
by
high
dimensionality
spaces
computational
inefficiencies.
This
review
highlights
how
AI-driven
techniques
provide
transformative
solutions
enabling
efficient
exploration
vast
spaces,
optimizing
intricate
parameter
systems,
predicting
performance
advanced
materials
with
accuracy.
By
bridging
gap
between
complexity
practical
implementation,
AI
accelerates
discovery
novel
functionalities.
Furthermore,
we
delve
into
emerging
domains,
such
as
diffractive
neural
networks
quantum
machine
learning,
emphasizing
their
potential
to
exploit
photonic
properties
innovative
strategies.
The
also
examines
AI’s
applications
areas,
e.g.,
optical
image
recognition,
showcasing
its
role
device
integration.
facilitating
development
highly
efficient,
compact
devices,
these
AI-powered
paving
way
next-generation
systems
enhanced
functionalities
broader
applications.
Advanced Engineering Materials,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 4, 2025
Acoustic
metastructures
(AMs)
are
a
type
of
artificial
engineering
materials
composed
various
micro–meso
structure
subwavelength
units.
They
can
exhibit
distinct
and
exotic
performances
such
as
low
mass,
volume,
frequency,
broadband
through
appropriate
structural
designs,
which
provide
novel
means
for
the
exploration
physical
interpretation
in
terms
individual
case.
Thus,
design
strategies
AMs
unprecedented
properties
growing
interest
attention.
Beginning
with
recent
advances
design,
comprehensive
review
mechanisms
characteristics
four
typical
AMs,
i.e.,
Helmholtz
resonators,
membrane‐type
coiling‐up
space
structures,
lattice
is
performed.
Meanwhile,
application
potentials
associated
regard
to
performance
evolutions
including
sound
absorption
noise
reduction,
acoustic
cloaking,
lenses
introduced,
well
corresponding
optimization
strategies.
Finally,
current
scientific
technical
challenges
developmental
trends
summarized.
This
work
aims
roadmap
next‐generation
trigger
on
unsuspected
mechanisms.
