Thank
you
for
watching
this
presentation
for
the
Europe
Discovery
Summit
Conference
online.
My
name
is
Damien
Perret.
I
am an
R&D
scientist
at
CEA
in
France.
I
am
with
Francois
Bergeret
the
statistician
and
the
founder
of
Ippon
Innovation
in
France.
With
Francois,
we
are
very
happy
to
be
here
today
and
we
would
like
to
thank
the
steering
committee
who
gave
us
the
opportunity
to
talk
about
this
work
which
is
about
innovative
approaches
using
JMP.
We
will
give
you
two
case
studies.
Francois
will
present
the
first
case
and
I
will
present
the
second
case.
Just
start
with
a
few
words
about
CEA.
CEA
is
a
French
government
organization
for
research,
development,
and
innovation
in
four
areas;
low
carbon
energies,
technological
research,
fundamental
research,
and
defense
and
security.
The
CEA
counts
about
20,000
people
and
we
are
located
on
nine
different
sites
in
France.
We
have
strong
relationships
with
the
academic
world
and
many
collaborations
with
universities
and
partners,
both
in
France
and
all
around
the
world.
A few
words
about
Ippon
Innovation.
Of
course,
we
are
a
smaller
company
compared
to
the
CEA.
It
was
created
15
years
ago.
We
are
based
in
the
South
of
France,
Toulouse.
We
are
a
team
of
statistician,
only
statisticians
with
skill
in
industrial
statistics,
example,
SPC,
measurement
system
analysis,
and
of
course,
machine
learning
and
so
on.
I'm
a
JMP
user
since
1995,
so
a
long
time
ago.
I
started
with
JMP
3.
Of
course,
Ippon
is
JMP
Partner
because
we
used
a
lot
of
JMP.
For
example,
for
yield
optimization,
we
have
a
tool
called
Yeti,
automatic
yield
optimization
in
complex
manufacturing
systems.
We
also
developed
solution
based
on
a
customer
request,
what
we
call
software
on
demand,
for
example,
a
full
solution
for
outlier
detection
or
statistical
process
control.
Using
JMP
and
GSL,
we
have
several
GSL
expert
here,
including
Carole
Soual,
which
is
co-
author
of
this
talk
today.
We
also
have
a
classical
consulting
and
training
expertise
based
on
JMP
on
industrial
statistics.
The
content
of
the
presentation
today,
we
will
present
two
real
case
studies
with
Damien.
The
first
one
is
based
on
simulation
and
a
computer
design
of
experiment,
and
the
second
one
will
be
presented
by
Damien
on
a
machine
learning
tool
for
prediction.
I
will
present
the
case
study
number
1
based
on
mixture.
It
is
okay.
You
can
go
on
the
next
slide.
The
context
is
a
risk
assessment
and
a
probability
calculation.
To
explain
it
a
little
bit,
mixture
design
of
experiment
was
created
by
the
CEA
to
evaluate
the
performance
of
a
material
for
nuclear
waste.
The
conditioning
is
done
with
salts
in
a
matrix.
The
performance
is
determined
by
a
threshold
on
the
energy.
The
energy
has
to
be
higher
than
the
threshold.
It
is
not
so
easy
to
estimate
the
probability
to
be
below
this
threshold.
We
will
use
all
the
tools
and
all
the
data
that
we
have
to
do
this
task.
Of
course,
the
probability
has
to
be
as
small
as
possible.
Damien,
the
CEA
expert,
will
assess
if
this
probability
is
okay.
Now,
what
methodology
we
use
to
estimate
this
worst
case
probability?
First
of
all,
based
on
the
data
from
the
mixture
design
of
experiment,
we
have
estimated
several
models.
Basically,
some
classical
linear
models,
also
classification
and
decision
trees,
and
also
neural
networks
are
the
main
models
used.
For
each
model,
we
have
done
three
analysis.
First,
the
Monte
Carlo
simulation
on
the
factor,
so
classical
random
simulation
with
Monte
Carlo.
Also,
what
we
call
space
filling
design,
a
computer
design
with
JMP,
where
we
try
to
explore
the
design
space
but
by
computer
simulations,
adding
a
noise
on
the
response.
This
is
done
with
JMP
Pro.
Last
thing
we
did
is
a
blending
of
Monte
Carlo
and
space
filling
design.
We
will
detail
this,
but
this
is
very
useful
as
we
want
to
estimate
a
worst
case
probability.
We
can
go
to
next
slide.
Case
study
number
1,
classical
JMP
simulation
for
the
mixture
DOE.
First
of
all,
we
have
to
select
the
best
model.
Before
doing
the simulation,
we
need
to
find
the
best
model.
There
is
a
very
nice
feature
in
JMP
Pro,
which
is
called
model
comparison.
Very
quickly,
you
can
compare
the
models
based
on
criteria,
RS
quare or
the
AISC.
We
have
done
this
and
I
will
do
the
demo
right
now.
I
share
my
screen
now.
I
have
to
share
my
screen.
We
have
to
use
JMP.
Here,
I
open
the
data
set.
This
is
a
data
set
from
mixture
design
of
experiment.
There
is
eight
factors,
X
1-
X8,
and
the
response
is
the
energy.
To
show
you
an
example
of
the
model
that
we
did,
we
perform
here,
for
example,
a
predictive
modeling
neural
network.
The
response
is
the
energy.
The
X's
factors
are
here.
We
click
here.
For
information,
we
decide
here
not
to
divide
the
full
sample
in
learning
sample,
validation
sample,
test
sample,
because
data
are
from
design
of
experiments,
so
we
have
not
a
lot
of
data.
Thirty-one
experiment
maximum
here.
We
use
a
KFold
validation
to
save
samples,
I
would
say.
A
very
simple
neural
net
here,
one
layer,
and
we
click
on
Go.
There
is
quite
a
good
model
with
a
correct,
let's
say,
RS
quare
for
that.
How
is
working
the
comparison
of
the
model
in
JMP?
You
need
to
save
the
prediction
formula.
You
click
on
the
spot
and
I
save
the
formula.
Doing
this,
you
have
formulas
here
of
the
neural
net
with
hidden
layers.
You
see
the
formula,
hyperbolic
tangent
here
of
the
linear
predictor.
Second n
euron
here
of
the
hidden
layers,
another
formula,
and
so
on.
At
the
end,
for
the
neural
net,
the
final
formula
for
the
prediction
is
a
linear
combination
of
the
hidden
neurons.
I
keep
this
formula
for
the
moment,
and
I'm
going
to
do
the
same
with
a
linear
model.
The
linear
model
was
saved
here
to
save
time.
We
decide
to
have
a
linear
model.
For
mixture
design,
we
have
some
cross
effect
here
and
a
linear
model.
Of
course,
we
need
to
clean
the
model.
We
need
to
remove
what
is
not
significant
here
and
so
on.
When
the
job
is
done,
we
also
have
to
save
the
formula.
I
saved
what
I
call
the
prediction
formula.
Here,
once
again,
in
the
JMP
table
somewhere,
you
will
have
the
prediction
formula
for
the
energy,
ordinary
least
square.
You
have
a
classical
linear
formula
for
the
linear
model.
To
summarize,
here
I
have
two
models,
neural
network
and
ordinary
least
square,
and
I
have
a
formula
for
the
two
models.
Then
with
these
formulas,
I
go
to
Analyze,
Predictive
Modeling,
and
Model
Comparison.
This
is
the
nice
JMP
platform
for
machine
learning.
Here
what
you
have
to
enter
is
just
the
formula
of
the
predictor.
Here,
for
the
example,
I
just
enter
two
formula,
ordinary
least
square
and
neural net,
and
that's
all.
You
just
click
Okay.
Here
you
have
a
comparison
model
with
some
criteria
and
you
select
the
best
model h
ere.
First
of
all,
I
add
noise
in
the
data,
so
RS
quare
are
not
so
good.
In
addition,
we
can
compare
the
both
model.
In
that
case,
for
the
worst
case
analysis,
we
decided
decided
to
perform
the
simulation
both
on
linear
model
and
also
on
neural
nets.
Now,
we
are
going
to
do
that
work.
L
et
me
show
you
what
we
did.
Maybe
I'm
going
to
share
this
slide,
Damien,
because
I
need
to
show
something
here.
It
will
be
easier
with
my
PC.
So the
live
demo
is
done.
Here
I'm
going
to
use
a
Monte
Carlo
simulation.
Very
easy
with
a
JMP
prediction
profiler.
First
of
all,
this
is
my
first
demo.
After
that,
I
will
do
the
mixture
design
of
experiment
space
filling
design.
I
have
to
say
that
both
for
Monte
Carlo
and
also
for
space
filling
design,
we
have
some
mixture
constraints.
It
means
that
the
sum
of
the
component
has
to
be
equal
to
one.
It's
not
a
real
space
filling
design.
It's
not
a
real
Monte
Carlo
because
you
have
this
constraint.
J
MP
has
a
smart
algorithm,
iterative
algorithm
to
take
into
account
the
constraints.
We
are
going
to
simulate
Monte
Carlo
with
the
first
factors
and
then
with
the
second
factors
taking
into
account
the
first
one
and
so
on.
At
the
end,
I
will
present
the
full
simulation
with
both
Monte
Carlo
and
space
filling
design.
This
is
new
because
what
we
have
done
is
for
each
run
of
the
space
filling
design,
we
have
done
1,000
Monte
Carlo
simulations.
It's
really
a
worst
case
here,
but
it
was
the
objective
for
the
CEA
to
have
the
worst
case.
Thanks
to
this,
we
will
get
a
really
good
estimation
of
the
worst
case
probability.
Now,
let
me
jump
to
the
demo.
First
of
all,
Monte
Carlo.
Here
I
have
the
result
for
the
Monte
Carlo.
No,
sorry,
it's
not
the
right
one.
Here
it
is.
Let's
use
the
neural
network.
Here,
for
the
neural network,
you
can
ask
for
the
profiler.
If
you
ask
for
the
profiler,
here
you
can
ask
the
simulator.
I
will
randomly
move
the
factors.
But
what
is
important
here,
I'm
asking
for
uniform
distribution
because
it's
more
classical
for
mixture
design.
Here,
I'm
going
to
simulate
random
data
for
the
factor
X1
,
but
between
this
value
and
this
value
because
there
is
a
constraint
here.
I
continue
with
the
second
factor
with
uniform
simulation
between
this
value
and
this
value,
and
so
on.
To
save
time,
I
will
automatically
run
the
simulation
here.
Here
it
is.
Sorry,
I
don't
have
the
right
data
set.
Sorry
about
that.
W
here
it
is.
Model
comparison
and
simulation.
Here
it
is.
Okay,
here
it
is,
Monte
Carlo
simulation.
To
save
time,
I'll
show
you
directly
the
result
with
a
random
simulation
with
a
mixture
constraint.
W
e
have
done
this
Monte
Carlo
simulation
and
we
have
this
result
on
the
energy.
What
is
nice
also
is
that
you
can
put
the
result
in
a
table.
This
is
the
result,
10,000
Monte
Carlo
simulation.
For
each
simulation
and
with
the
model,
you
have
the
energy.
Now,
to
calculate
the
probability
to
be
lower
than
the
spec
limit,
we
just
have
to
do
a
distribution
of
the
simulated
energy.
This
is
a
distribution,
not
exactly
Gaussian,
more
close
to
a
Laplace
distribution.
But
anyway,
it
doesn't
matter.
We
will
do
the
process
capability
and
the
spec
given
by
the
CEA
is
minus
100.
I
just
click
on
this
and
we
have
what
we
call
the
capability
analysis,
overall
capability.
Ppk
is
quite
good,
higher
than
1.3
.
Here,
the
expected
percentage
of
out
of
spec
is
very
low
because
this
number
is
clearly
very
low.
We
can
have
a
look
in
a
scientific
notation.
We
are
at
a
very
small
probability
and
clearly
it
was
acceptable
for
the
[inaudible 00:15:13]
.
At
this
time,
based
on
neural
network
model,
based
on
Monte
Carlo
simulation,
we
have
estimated
the
first
probability
to
be
out
of
specification
and
this
probability
is
here.
Next
step.
What
we
have
done
here
is
that
we
are
going
back
here.
Sorry,
I
don't
close
this.
I
don't
close
this.
I'm
going
here
to
do
another
simulation
with
what
we
call
a
simulation
experiment.
Simulation
experiment
is
also
called
sometimes
a
space
filling
experiment.
You
try
to
explore
the
design
space.
Here,
we
have
to
remind
that
there
is
a
mixture
constraint,
so
we
will
not
explore,
of
course,
a
full
design
space,
but
we
will
explore
a
part
of
the
design
space
here.
Here
is
a
result,
128
computer
runs
with
the
simulation
data.
We
can
have
a
look
at
the
result
of
the
simulation.
If
we
do
a
scatterplot
matrix
on
the
simulated
experiment
on
the
factors,
here
is
the
exploration
of
the
factor
with
a
mixture
constraint.
With
this,
once
again,
we
have
simulated
energy
but
here,
it's
not
Monte
Carlo
simulation,
it's
a
computer
simulation
experiment.
Same
job,
we
can
put
the
energy
here.
W
here
is
the
simulated
energy?
Here
it
is.
Once
again,
roughly
normally
distributed.
Here
I'm
going
to
calculate,
once
again,
the
process
capability
with
a
spec
minus
100.
Here
it
is.
Here
I
should
have
the
process
capability
which
is
good.
Once
again,
you
have
a
probability
to
be
out
of
the
spec,
which
is
close
to
7
per
1,000.
It
is
also
acceptable
as
a
result.
But
you
can
see
that
in
that
case,
the
probability
is
higher
than
the
previous
one
because
the
calculation
was
different
and
because
it
was
a
simulation
experiment
exploring
the
space
and
it's
different
from
Monte
Carlo
simulation.
Last
thing
that
we
did,
and
I'm
going
to
open
the
d
ata
file.
Here
it
is,
Simulated
Monte
Carlo.
Here
it
is.
Not
the
right
one,
sorry.
I
have
a
lot
of
things
open.
Here
it
is.
What
is
this
file?
What
we
did
here.
We
did
both
a
space
filling
design
and
for
each
run
of
the
space
filling
design,
we
have
done
1,000
Monte
Carlo
simulations.
The
total
number
of
points
here
is
128,000
lines.
Both
Monte
Carlo
and
also
a
computer
design
of
experiment.
Here
we
have
really
a
good
data
set
with
all
the
potential
variations.
Some
are
forced
by
the
design
of
experiment,
others
are
clearly
random
with
the
Monte
Carlo.
Here,
once
again,
we
are
going
to
estimate
the
probability.
You
have
the
nice
distribution
of
the
energy.
Then
we
will
once
again,
calculate
the
probability
to
be
out
of
spec.
Entering
the
spec
here,
here
it
is.
Here
we
have
a
probability
to
be
out
of
spec,
which
is
close
to
1
per
1,000.
Once
again,
it
was
quite
a
good
result
here.
This
result
is
quite
innovative.
Here,
just
for
information,
we
had
to
create
a
little
JMP
script
here
to
do
this,
to
mix
the
computer
design
and
the
Monte
Carlo
simulation.
There
is
a
little
GSL
code
for
that.
That's
all
for
my
part.
Damien,
maybe
you
can
go.
I
stopped
the
sharing.
Okay. This
is
now
the
case
study
number
2.
For
this
study,
we
have
developed
a
custom
tool
for
predictive
application.
The
objective
here
was
to
create
a
tool
including
statistical
models
in
JMP
Pro
in
order
to
predict
a
specific
property
which
is
the
glass
visco
sity
as
a
function
of
composition
and
temperature.
To
do
that,
experimental
data
are
coming
from
both
commercial
database
and
from
our
own
database
at
CEA
.
A
s
we
will
see,
the
originality
of
the
approach
comes
from
the
methodology
for
data
sub
sampling.
We
wanted
the
algorithms
to
be
coded
in
GSL
and
implemented
in
JMP
Pro.
The
response
of
the
model
is
the
glass
viscosity,
of
course,
and
the
factors
are
the
weight
percent
of
the
glass
components.
Here
are
some
background
information.
You
have
to
know
that
glass
material
is
a
non-
crystalline
solid
and
it
is
obtained
by
a
rapid
quench
of
a
glass
melt.
From
a
material
point
of
view,
glass
is
a
mixture
of
different
oxides.
The
number
of
oxides
is
variable
from
two
or
three
in
a
very
simple
glass
to
about
30
and
even
more
in
the
most
complex
compositions.
T
here
is
a
long
tradition
in
the
calculation
of
glass
properties,
and
we
think
that
the
first
models
were
created
in
Germany
at
the
end
of
the
19th
century.
Since
then,
the
amount
of
published
literature
in
the
field
of
glass
property
prediction
has
increased
a
lot.
So
that
today,
we
have
a
huge
amount
of
glass
data
available
in
commercial
database.
Several
challenges
remain
for
the
prediction
of
the
glass
v
iscosity
because
the
glass
v
iscosity
is
a
property
that
is
very
difficult
to
predict.
First,
v
iscosity
has
a
huge
range
of
variation
up
to
certain
orders
of
magnitude.
A
lso,
v
iscosity
is
very
dependent
on
physical
and
chemical mechanisms
that
can
occur
in
the
glass
melt,
depending
on
the
glass
composition,
like
fast
separation
or
crystallization,
for
example.
Here
is
just
a
short
example
that
shows
this
difficulty.
We
have
selected
three
compositions
of
what
we
call
SBN
glass,
which
is
very
simple
glass
with
only
3 oxides.
W
e
applied
the
best
known
models
from
the
literature
to
calculate
the
visco
sity.
T
hen
we
compare
the
predicted
values
with
the
experimental
value
we
have
measured
with
our
own
device.
You
can
see
that
even
for
a
very
simple
glass,
it
is
not
easy
to
obtain
one
reliable
value
for
the
predicted
v
iscosity.
Here
is
a
good
picture
we
like
to
use
to
give
a
view
of
the
database
where
each
dot
is
one
glass
in
a
multi
dimensional
view
of
the
domain
of
compositions.
Data
may
come
from
different
isolated
studies,
or
we
can
have
data
coming
from
studies
using
experimental
design,
or
data
obtained
from
parametric
studies
with
variation
of
one
component
at
a
time.
W
e
spent
a
lot
of
time
in
the
past
to
apply
different
method
of
machine
learning.
A
classical
approach
was
used
for
partitioning
the
data
into
a
training
set
and
a
validation
set.
But
at
the
end,
no
statistical
model
with
acceptable
predictive
capability
was
found
to
predict
the
v
iscosity.
T
hat's
why
we
have
decided
to
use
a
different
approach.
Instead
of
using
all
the
data,
we
think
it
is
better
to
create
a
model
by
using
data
close
to
the
composition
where
we
want
to
predict
the
v
iscosity.
So
for
example,
if
we
want
to
predict
here,
one
model
will
be
created
from
the
data
we
have
in
this
area,
and
a
different
model
will
be
created
if
we
want
to
predict
the
property
here,
for
example.
T
hat's
why
we
say
that
this
technique
is
dynamic
because
the
model
depends
on
the
composition
and
it
is
related
and
fitted
where
we
want
to
predict.
W
e
say
that
the
model
is
automatic
because
we
don't
have
to
do
this
manually.
Every
step
is
done
by
a
algorithm
implemented
in
the
tool.
O
ne
of
the
most
important
point
is
certainly
the
determination
of
the
optimal
data
set
to
create
the
model.
F
or
that,
we
have
implemented
two
methods
of
sub
sampling.
In
the
first
method,
a
theoretical
or
virtual
design
of
experiment
is
generated
around
the
composition
of
interests.
Then
each
run
of
the
design
is
replaced
by
the
most
similar
experimental
data
present
in
the
database,
leading
to
the
final
training
data
set.
In
the
second
method,
we
have
implemented
in
the
tool. Th
is
is
based
on
different
size
of
data
sets
created
around
the
composition
of
interest.
A
small
data
set
is
generated
by
the
tool
and
model
are
created
on
this
small
subset
to
predict
the
visco
sity.
T
hen
bigger
and
bigger
data
sets
are
generated
automatically
and
the
optimal
size
is
evaluated
by
several
statistical
criteria
associated
to
each
subsets.
Finally,
the
construction
of
the
models
is
based
on
three
different
algorithms
implemented
in
the
tool.
First
is
a
polynomial
model
obtained
by
a
multi-
linear
regression.
Second
is
a
genreg
model
and
neural
net
model.
A
t
the
end,
we
have
six
different
calculated
values
which
makes
the
prediction
very
robust.
Let's
go
to
JMP
to
see
how
it
works.
L
et
me
first
show
you
the
code.
T
he
script,
as
you
can
see,
is
quite
long,
about
700
rows,
which
is a quite
complicated
GSL
code.
The
first
thing
you
have
to
do
is
to
enter
the
composition
of
the
glass
you
want
to
predict
the
viscosity.
T
o
do
that,
you
can
use
an
interface
we
have
created.
You
just
have
to
select
the
oxide
entering
the
composition.
F
or
each
oxide,
you
just
have
to
enter
the
weight
percent.
Or
if
you
want,
you
can
directly
enter
the
composition
in
the
script,
which
is
a
little
bit
more
quick,
I
would
say.
Then
you
launch
the
script.
I
won't
do
that
now
because
it
takes
about
one
or
two
minutes.
It's
not
very
long.
But
for
this
demo,
I
have
already
run
the
script.
Let
me
show
you
the
results.
At
the
end
of
the
calculation,
you
have
this
window
where
you
can
get
a
statistical
report.
Very
interesting.
First,
here
you
have
the
composition
you
have
entered,
it's
just
a
reminder.
W
e
have
the
graph
showing
the
predicted
value.
O
n
the
Y
axis,
we
have
the
predicted
values
of
the
viscosity
calculated
by
the
three
algorithm
and
for
the
two
methods.
On
the
X
axis,
we
have
the
number
of
enlargement
for
the
second
method
I
have
described.
I
n
red,
which
is
the
most
important
value,
I
would
say,
is
the
average
of
all
the
different
predictions.
I
t
is
the
best
prediction,
I
would
say,
of
the
glass
viscosity
.
I
f
we
need
to
have
more
statistical
details,
we
have
a
lot
of
information
in
this
report
to
study
the
quality
of
each
model.
For
example,
we
can
check
the
values
of
the
PRESS
statistics
for
the
multi-
linear
BIC
F
model.
For
example,
here
we
can
see
that
the
PRESS
values
tell
us
that
the
prediction
using
the
method
number
1
is
a
little
bit
better
than
for
the
second
method.
W
e
also
see
that
the
model
degradation
with
the
enlargement
of
the
training
set.
We
can
also
check
the
RS
quare
values
for
the
two
different
method
and
for
the
different
algorithm,
and
we
can
compare
them.
W
e
can
have
even
more
details
on
the
design
of
experiments
that
were
created
and
all
the
formula
of
prediction.
T
his
is
a
lot
of
information,
but
there
is
the
most
important
part
here,
which
is
the
predicted
value
of
the
viscosity.
Let's
go
back
to
the
PowerPoint.
T
his
is
the
result
obtained for
the
simple
SBN
glass,
which
is,
as
I
said
earlier,
a
simple
glass
with
only
3
oxides.
But
we
have
calculated
the
visco
sity for
three
different
composition
of
SBN
glass.
W
e
compare
the
results
obtained
from
our
tool
here
with
the
results
from
the
models
available
in
the
literature.
In
term
of
the
relative
error
of
prediction,
it
has
to
be
as
low
as
possible.
W
e
can
see
that
the
best
results
of
prediction
are
obtained
with
our
tool,
which
is
really
great.
H
ere,
we
have
the
results
obtained
on
more
complex
glasses.
T
he
tool
predictive
capability
was
evaluated
by
extracting
230
rows
from
the
global
database.
In
this
table,
we
have
the
relative
error
of
the
visco
sity
prediction
for
different
type
of
glass
and
for
the
global
subset
of
data.
Three
quantiles
are
given,
the
median,
meaning
that
50
%
of
the
predicted
values
have
a
relative
error
that
is
below
the
value
indicated
here.
W
e
also
give
the
75
%
and
the
90
%
quantiles.
W
hen
we
talk
about
glass
v
iscosity,
traditionally,
we
consider
that
the
predictive
error,
around
30
%
is
very
good.
W
e
see
that
for
the
majority
of
the
data,
the
model
capability
is
fine
and
we
were
very
happy
with
the
results
we
obtained.
Here
are
some
very
important
key
parameters.
It
is
very
important
to
take
into
account
as
many
inputs
from
the
glass
experts
as
possible.
For
example,
we
had
to
create
specific
algorithms
to
handle
the
nature
and
the
wall
of
oxides
and
viscos
ity.
Another
point
of
major
importance
is
related
to
the
origin
and
to
the
reliability
of
the
data.
For
this,
a
significant
amount
of
time
in
this
project
has
been
spent
to
the
constitution
of
a
reliable
database.
W
e
had
to
implement
ways
and
we
had
to
study
different
ways
of
calculating
the
distances
between
the
glass
composition.
I
t's
time
to
conclude
now.
We
have
presented
two
different
case
studies.
In
the
first
case
studies,
we
have
created
several
models
and
we
have
compared
them
for
the
risk
assessment.
We
have
seen
that
it
was
easy
with
JMP
to
perform
Monte
Carlo
simulations,
even
for
mixture
designs
with
constraints.
We
have
seen
that
it
was
easy
also
to
perform
space
filling
design,
and
again,
even
for
mixture
designs.
By
combining
Monte
Carlo
and
space
filling
designs,
worst
case
probability
has
been
estimated.
I
n
the
second
study,
we
have
presented
the
tool
we
have
created
with
an
original
method
of
subsampling
the
data.
For
each
composition
of
interest,
a
specific
model
of
viscosity
was
constructed
around
this
composition,
and
we
have
seen
that
the
prediction
accuracy
on
the
visco
sity
was
very
promising
and
much
better
than
models
available
in
the
literature.
Thank
you
for
your
attention.
