Landslides,
Journal Year:
2023,
Volume and Issue:
20(9), P. 1853 - 1863
Published: May 27, 2023
Abstract
In
this
study,
a
new
paradigm
compared
to
traditional
numerical
approaches
solve
the
partial
differential
equation
(PDE)
that
governs
thermo-poro-mechanical
behavior
of
shear
band
deep-seated
landslides
is
presented.
particular,
paper
shows
projections
temperature
inside
as
proxy
estimate
catastrophic
failure
landslides.
A
deep
neural
network
trained
find
temperature,
by
using
loss
function
defined
underlying
PDE
and
field
data
three
To
validate
network,
we
have
applied
following
cases:
Vaiont,
Shuping,
Mud
Creek
The
results
show
that,
creating
training
with
synthetic
data,
landslide
can
be
reproduced
allows
forecast
basal
case
studies.
Hence,
providing
real-time
estimation
stability
landslide,
other
solutions
whose
study
has
calculated
individually
for
each
scenario.
Moreover,
offers
novel
procedure
design
architecture,
considering
stability,
accuracy,
over-fitting.
This
approach
could
useful
also
applications
beyond
Research Square (Research Square),
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 16, 2025
Abstract
Maximizing
output
from
renewable
solar
panels
requires
higher
efficiency.
Conventionally,
such
optimization
techniques
-
MPPT
(Maximum
Power
Point
Tracking)
along
with
heuristic
algorithms
suffer
significantly
slow
adaptability
and
track
sub
optimality
under
dynamic
environments.
This
article
proposes
a
numerical
modeling
framework
hybrid
AI
models,
combining
physics-informed
neural
networks
RL
for
real-time
of
orientation
in
panels.
The
methodology
uses
precise
energy
transformation
analysis,
deep
learning-based
dynamically
adjusts
the
angles
to
maximize
power
output.
A
self-learning
adaptive
network
is
developed
improve
tracking
accuracy
based
on
irradiance
temperature
variations.
Moreover,
an
Edge
architecture
introduced
make
low-latency
decisions
reduced
dependency
cloud
computation,
thus
improving
efficiency
system.
Besides,
advanced
model
CNN-LSTM
applied
forecasting
predictive
control
maximum
yield.
Experimental
validation
was
performed
using
UTL
335W
330W
PV
modules,
where
data
acquisition
followed
by
AI-driven
optimization.
Results
show
increase
yield
10–15%
compared
traditional
systems,
while
computations
are
40–50%
faster
AI-based
modeling.
proposed
approach
achieves
25%
lower
error
(RMSE/MAE)
30%
consumption
through
implementation.
study
sets
up
new
paradigm
AI-integrated
optimization,
which
ensures
enhanced
performance
practical
deployment.
findings
advance
intelligent
set
benchmark
management.
International Journal for Numerical Methods in Engineering,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 9, 2024
ABSTRACT
To
obtain
fast
solutions
for
governing
physical
equations
in
solid
mechanics,
we
introduce
a
method
that
integrates
the
core
ideas
of
finite
element
with
physics‐informed
neural
networks
and
concept
operators.
We
propose
directly
utilizing
available
discretized
weak
form
packages
to
construct
loss
functions
algebraically,
thereby
demonstrating
ability
find
even
presence
sharp
discontinuities.
Our
focus
is
on
micromechanics
as
an
example,
where
knowledge
deformation
stress
fields
given
heterogeneous
microstructure
crucial
further
design
applications.
The
primary
parameter
under
investigation
Young's
modulus
distribution
within
system.
investigations
reveal
physics‐based
training
yields
higher
accuracy
compared
purely
data‐driven
approaches
unseen
microstructures.
Additionally,
offer
two
methods
improve
process
obtaining
high‐resolution
solutions,
avoiding
need
use
basic
interpolation
techniques.
first
one
based
autoencoder
approach
enhance
efficiency
calculation
high
resolution
grid
points.
Next,
Fourier‐based
parametrization
utilized
address
complex
2D
3D
problems
micromechanics.
latter
idea
aims
represent
microstructures
efficiently
using
Fourier
coefficients.
proposed
draws
from
deep
energy
but
generalizes
enhances
them
by
learning
parametric
without
relying
external
data.
Compared
other
operator
frameworks,
it
leverages
domain
decomposition
several
ways:
(1)
uses
shape
derivatives
instead
automatic
differentiation;
(2)
automatically
includes
node
connectivity,
making
solver
flexible
approximating
jumps
solution
fields;
(3)
can
handle
arbitrary
shapes
enforce
boundary
conditions.
provided
some
initial
comparisons
well‐known
algorithms,
emphasize
advantages
newly
method.
Landslides,
Journal Year:
2023,
Volume and Issue:
20(9), P. 1853 - 1863
Published: May 27, 2023
Abstract
In
this
study,
a
new
paradigm
compared
to
traditional
numerical
approaches
solve
the
partial
differential
equation
(PDE)
that
governs
thermo-poro-mechanical
behavior
of
shear
band
deep-seated
landslides
is
presented.
particular,
paper
shows
projections
temperature
inside
as
proxy
estimate
catastrophic
failure
landslides.
A
deep
neural
network
trained
find
temperature,
by
using
loss
function
defined
underlying
PDE
and
field
data
three
To
validate
network,
we
have
applied
following
cases:
Vaiont,
Shuping,
Mud
Creek
The
results
show
that,
creating
training
with
synthetic
data,
landslide
can
be
reproduced
allows
forecast
basal
case
studies.
Hence,
providing
real-time
estimation
stability
landslide,
other
solutions
whose
study
has
calculated
individually
for
each
scenario.
Moreover,
offers
novel
procedure
design
architecture,
considering
stability,
accuracy,
over-fitting.
This
approach
could
useful
also
applications
beyond
