Greetings, I am Humphrey Yang
from Carnegie Mellon
Today, I am here to present SimuLearn
a fast and accurate simulator to support
morphing material design and workflows.
The HCI community has recently become interested in
using active materials to enable
new modalities of interactions
but designing them is challenging
because we need to account for
their transformations
And that is why we need simulations in our design tools
but existing simulation methods each has their pros and cons
there is a tradeoff between speed and accuracy
On one end
parametric geometry ignores physical factors
to achieve fast computation
and they are less accurate
We also have mass-spring models or elastic rods
that consider some physical factors and are more accurate
but they are also slower to compute
and are subjected to specific limitations
On the opposite end
finite element analysis is highly accurate
but they are also very slow to compute
and are not interactive
Therefore, we are tasked to develop a fast and accurate
simulation method for morphing materials
This method should be fast enough
to afford real-time interactions
while being sufficiently accurate
to inform effective design decisions
The concept is fairly simple
Given a material system
we use FEA to collect accurate data
about its transformations
then extract useful information
to arrive at a concise dataset
This dataset is then used to
train machine learning models
so that when the model is trained
it will be able to simulate fast and accurately
and we can use that simulator to make design tools
In this work
we use thermoplastic two by two morphing grids
to contextualize this concept
The grids are made of bending beams
and passive joints
and are always fixed at the center
during transformations
We used a parametric script to generate
different input models for FEA
These models have varying design parameters
to cover the design space
The FEA solver is provided by literature
and has an accuracy higher than 95%
We collected more than two thousand trials
for training the final simulator
Raw FEA results are distilled into
concise feature representations
The beams are represented in terms of
their shapes, stressfields, and actuator designs
whereas the joints are only described by
their shapes and boundary conditions
because of their passiveness
We can also represent the grid’s structure
as an abstract graph
in which the joints are the nodes
and the beams are the edges
This graphical representation
also allows us to use graph convolutional networks
as our machine learning architecture
which accounts for the dynamics
between elements
At a high level
the forward computation can be
characterized into several steps
Given a grid as input
we decompose it into beams and joints
represented by feature vectors
These vectors are then used by the network
to compute their updates
By applying the updates to the elements
we can then obtain their shape
in the next time step
as well as the grid's as a whole
and we can iterate this process
until there is no further transformation
Finally, when we benchmarked the simulator
we find that it is a thousand times faster
than FEA while being similarly accurate
When compared with physical prototyping
SimuLearn is also nine thousand times faster
and provides very accurate predictions
It is also highly parallelizable
when running on a GPU
In design tasks
SimuLearn also allows us to
to extend design spaces to those
that require high accuracy
and afford workflows that require
different levels of interactivity
In a forward design workflow
SimuLearn can help users to iterate
and explore design options without waiting
In our joinery design example
the designer used our tool to visualize
interlocking behaviors and created multiple
design alternatives in a very short amount of time
We can also use SimuLearn in inverse design tasks
by leveraging its speed and parallelizability
In our modular lampshade examples
the design tool used finite difference methods
to bring the shape-fitting errors to an
acceptable range within 12 minutes
much faster than manual design would
We can also make optimization tasks more interactive
by putting the user in the loop
In our aggregation table design
if the designer wants to modify the structure
the tool can offer candidate changes
for the user to choose from
and the user can make decisions
based on their aesthetic concerns
This way, the tool and the user can each
take on what they are good at
To sum up, SimuLearn can readily support
complex design tasks and interactive workflows
but there are still rooms for future research
such as generalizing it to other material systems
by swapping the FEA solver and representations
or scaling to different sizes
by augmenting the machine learning model
Looking forward into the future
we also think that SimuLearn will enable
future design tools to become more active
and conversational with the user
Finally, this is the end of this talk, thank you