Computer Methods in Applied Mechanics and Engineering,
Journal Year:
2023,
Volume and Issue:
415, P. 116290 - 116290
Published: Aug. 3, 2023
Our
recent
intensive
study
has
found
that
physics-informed
neural
networks
(PINN)
tend
to
be
local
approximators
after
training.
This
observation
leads
this
novel
radial
basis
network
(PIRBN),
which
can
maintain
the
property
throughout
entire
training
process.
Compared
deep
networks,
a
PIRBN
comprises
of
only
one
hidden
layer
and
"activation"
function.
Under
appropriate
conditions,
we
demonstrated
PIRBNs
using
gradient
descendent
methods
converge
Gaussian
processes.
Besides,
studied
dynamics
via
tangent
kernel
(NTK)
theory.
In
addition,
comprehensive
investigations
regarding
initialisation
strategies
were
conducted.
Based
on
numerical
examples,
been
more
effective
efficient
than
PINN
in
solving
PDEs
with
high-frequency
features
ill-posed
computational
domains.
Moreover,
existing
techniques,
such
as
adaptive
learning,
decomposition
different
types
loss
functions,
are
applicable
PIRBN.
The
programs
regenerate
all
results
at
https://github.com/JinshuaiBai/PIRBN.
Journal Of Big Data,
Journal Year:
2023,
Volume and Issue:
10(1)
Published: April 14, 2023
Abstract
Data
scarcity
is
a
major
challenge
when
training
deep
learning
(DL)
models.
DL
demands
large
amount
of
data
to
achieve
exceptional
performance.
Unfortunately,
many
applications
have
small
or
inadequate
train
frameworks.
Usually,
manual
labeling
needed
provide
labeled
data,
which
typically
involves
human
annotators
with
vast
background
knowledge.
This
annotation
process
costly,
time-consuming,
and
error-prone.
every
framework
fed
by
significant
automatically
learn
representations.
Ultimately,
larger
would
generate
better
model
its
performance
also
application
dependent.
issue
the
main
barrier
for
dismissing
use
DL.
Having
sufficient
first
step
toward
any
successful
trustworthy
application.
paper
presents
holistic
survey
on
state-of-the-art
techniques
deal
models
overcome
three
challenges
including
small,
imbalanced
datasets,
lack
generalization.
starts
listing
techniques.
Next,
types
architectures
are
introduced.
After
that,
solutions
address
listed,
such
as
Transfer
Learning
(TL),
Self-Supervised
(SSL),
Generative
Adversarial
Networks
(GANs),
Model
Architecture
(MA),
Physics-Informed
Neural
Network
(PINN),
Deep
Synthetic
Minority
Oversampling
Technique
(DeepSMOTE).
Then,
these
were
followed
some
related
tips
about
acquisition
prior
purposes,
well
recommendations
ensuring
trustworthiness
dataset.
The
ends
list
that
suffer
from
scarcity,
several
alternatives
proposed
in
order
more
each
Electromagnetic
Imaging
(EMI),
Civil
Structural
Health
Monitoring,
Medical
imaging,
Meteorology,
Wireless
Communications,
Fluid
Mechanics,
Microelectromechanical
system,
Cybersecurity.
To
best
authors’
knowledge,
this
review
offers
comprehensive
overview
strategies
tackle
Journal of Computational Design and Engineering,
Journal Year:
2023,
Volume and Issue:
10(4), P. 1736 - 1766
Published: July 4, 2023
Abstract
Topology
optimization
(TO)
is
a
method
of
deriving
an
optimal
design
that
satisfies
given
load
and
boundary
conditions
within
domain.
This
enables
effective
without
initial
design,
but
has
been
limited
in
use
due
to
high
computational
costs.
At
the
same
time,
machine
learning
(ML)
methodology
including
deep
made
great
progress
21st
century,
accordingly,
many
studies
have
conducted
enable
rapid
by
applying
ML
TO.
Therefore,
this
study
reviews
analyzes
previous
research
on
ML-based
TO
(MLTO).
Two
different
perspectives
MLTO
are
used
review
studies:
(i)
(ii)
perspectives.
The
perspective
addresses
“why”
for
TO,
while
“how”
apply
In
addition,
limitations
current
future
directions
examined.
Computers & Structures,
Journal Year:
2024,
Volume and Issue:
297, P. 107342 - 107342
Published: April 4, 2024
This
paper
presents
a
literature
review
on
methods
for
enabling
real-time
analysis
in
digital
twins,
which
are
virtual
models
of
physical
systems.
The
advantages
twins
numerous,
including
cost
reduction,
risk
mitigation,
efficiency
enhancement,
and
decision-making
support.
However,
their
implementation
faces
challenges
such
as
the
need
data
analysis,
resource
limitations,
uncertainty.
focuses
reducing
computational
demands,
have
not
been
systematically
discussed
literature.
reviews
categorizes
tools
accelerating
modeling
phenomena
needs
twins.
Advanced Energy Materials,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 10, 2024
Abstract
This
review
highlights
recent
advances
in
machine
learning
(ML)‐assisted
design
of
energy
materials.
Initially,
ML
algorithms
were
successfully
applied
to
screen
materials
databases
by
establishing
complex
relationships
between
atomic
structures
and
their
resulting
properties,
thus
accelerating
the
identification
candidates
with
desirable
properties.
Recently,
development
highly
accurate
interatomic
potentials
generative
models
has
not
only
improved
robust
prediction
physical
but
also
significantly
accelerated
discovery
In
past
couple
years,
methods
have
enabled
high‐precision
first‐principles
predictions
electronic
optical
properties
for
large
systems,
providing
unprecedented
opportunities
science.
Furthermore,
ML‐assisted
microstructure
reconstruction
physics‐informed
solutions
partial
differential
equations
facilitated
understanding
microstructure–property
relationships.
Most
recently,
seamless
integration
various
platforms
led
emergence
autonomous
laboratories
that
combine
quantum
mechanical
calculations,
language
models,
experimental
validations,
fundamentally
transforming
traditional
approach
novel
synthesis.
While
highlighting
aforementioned
advances,
existing
challenges
are
discussed.
Ultimately,
is
expected
fully
integrate
atomic‐scale
simulations,
reverse
engineering,
process
optimization,
device
fabrication,
empowering
system
design.
will
drive
transformative
innovations
conversion,
storage,
harvesting
technologies.
