So, this a current design and then you compute the linear appro,

approximation of this residual function.

And this method called, called sensitivity analysis in structural analysis.

And then after computing linear approximation it's kind of easy,

to predict the, compute from 2D input to 3D shape.

Just by solving our linear system.

Which is faster.

Of course, this is an approximation near this design.

So, if you go farther away, then it's, it becomes too different.

So and your system.

So single linear appropriate is nothing now.

Users who're walking far away from the linear state.

It's a difference from the real physically realistic result gets bigger.

So in that case, we compute another approximation here right this way.

And then, so

we cache multiple linear approximations occasionally and then blend them.

So, in this case, if you have two examples and

if you blend these two, you get a very, very close approximation.

And this is still very fast to compute.

So that's what we do in internally.

So caching happens occasionally.

So, the user, the system continuously monitors.

Users the dragging operation.

And then when the users dragging getting far away, you create another cache.

Now you have two cache, and

you move far away from these two caches, the system will generate another.

So in this way, the system incrementally, the more and more caches.

So this is a summary.

So we presented garment design with concurrent physical simulation.

So we edit a 2D pattern.

And the system presents 3D draping results.

And then we have uprising sensite,

sensiivity analysis which is linear approximation.

And those are multiple caches before providing rapid

feedback without random simulation each time.