Alright, well, let's start with an easy question.
How many of you are wearing a Fitbit or an Apple Watch
or some other kind of health tracking device?
And how many of you have got a smartphone with you here today?
Maybe I should say how many of you have not?
The fact that so many of us have these technological marvels in our pockets
or on our body
is a sure sign of the revolution that's taking place in computing
over the last decade.
And I want you to think with me for a second
about the elements of that revolution.
So first off, are the data.
These devices are collecting data about our health, our movements,
our habits and more.
And what's really important
is that those data are not generic population data,
but they're data that are personalized to us,
each as an individual.
Second, and just as important, are the models.
Inside these devices are very powerful
mathematical and statistical models.
Some of these models are learned entirely from data,
perhaps a machine-learning model that has learned to classify
whether I'm running or walking or biking or sleeping.
Some of these models are based in physics,
such as a physiological model that describes the equations
that represent cardiac function or circadian rhythm.
And now where things get really interesting
is when we start to put the data and the models together.
Mathematically, this is known as data assimilation.
So we have data and we have models.
With data assimilation, we start updating the models
as new data are collected from the system.
And we don't do this update just once, but we do it continually.
So as the system changes,
as I get older and my circadian rhythm
or as my cardiac function is not what it once was,
the new data is collected and the models are evolving
and following along with me.
Now that data assimilation is really important
because it's what personalizes the models to me
and that then gets us to the fourth element,
which is the element of prediction.
Now that I have these personalized models,
it's so powerful
because I can now get predictions or recommendations
that are tailored to me as an individual
and that are tailored to my dynamically evolving state over my life.
So ... what I’m describing,
this working together of data and models,
is likely very familiar to all of you
because it's been driving your personal choices in retail and entertainment
and wellness for many years.
But what you might not know
is that a similar revolution has been taking place
in engineering systems.
And in engineering systems, the story is much the same.
We have data and we have increasing amounts of data
as sensors have become smaller, lighter, cheaper and more powerful.
In engineering, we also have models.
Our models are usually grounded in physics.
These models represent the governing laws of nature.
They're powerful models that let us predict
how an engineering system will respond.
What you see up here on the slide is a picture of the unmanned aircraft
that I have in my research group
that we use for a great deal of our research.
And for this aircraft,
we have powerful finite element models
that let us predict how the aircraft structure will respond
under different conditions.
So these models let us answer questions
like, will the structure of the aircraft hold together on takeoff
if I design it in this way?
Or, what happens if the aircraft wing gets damaged
and I continue to fly it aggressively?
Will the aircraft hold together?
And again, just like the Fitbit and the smartphone example,
we can put the data and the models together
to build a personalized model of the engineering system,
a personalized model of the aircraft.
And we call this personalized model a digital twin.
So what is a digital twin?
It is a personalized,
dynamically evolving model of a physical system.
And I want you to think about the digital twin of my aircraft.
So as I create that digital twin,
I'm going to be collecting data from the sensors on board the aircraft.
I'm going to be collecting data from inspections
I might make of the aircraft,
and I'm going to be assimilating that data into the models.
And what's really important
is that I’m not building a generic model of just any old Telemaster aircraft.
I am building a personalized model
of the very aircraft that is right now sitting in my garage
down the road in South Austin.
And so that digital twin will capture the differences,
the variability from my aircraft to say, my neighbor's aircraft.
And what's more, that digital twin will not be static.
It's going to change as my aircraft ages and degrades
and gets damaged and gets repaired.
We will be assimilating data all the time
and the digital twin will follow the aircraft through its life.
So this is incredibly powerful.
I want you to imagine now that you're an airline
or maybe in a few years’ time, you’re an operator
of a fleet of unmanned cargo delivery drones,
and imagine that you would have a digital twin like this
for every vehicle in your fleet.
And think about what that would mean for your decision making.
You could make decisions about when to maintain any one aircraft,
depending on the particular evolving state of that aircraft.
You could make decisions about how to optimally fly an aircraft
on any given day,
given the health of the aircraft, given the mission needs,
given the environmental conditions.
It would really let you optimally manage that fleet of aircraft.
So this idea of a digital twin is pretty neat.
The term “digital twin” was coined in 2010 in a NASA report.
But the idea, this idea of a personalized model
combining models and data, is much older.
And many people point to the Apollo program
as being one of the places
where digital twins were first put into practice.
So in the Apollo program, back in the '60s and the '70s,
NASA would launch Apollo spacecraft up into space,
and they would also deploy a simulator,
a virtual model on the ground in Houston,
to follow along on the mission.
And now this became very important
and it became very useful in the Apollo 13 mission.
And again, perhaps you all know the story because we've seen the movie.
In the Apollo 13 mission, the spacecraft suffered a malfunction.
It was very badly damaged.
It became stranded up in space.
And so the story goes that NASA were able to take the data from the real aircraft,
the physical twin stuck up in space,
feed it into the simulator and to the virtual models
on the ground in Houston,
do the data assimilation,
dynamically evolve the simulator
so now that it represented the conditions of the damaged spacecraft
and then use that simulator to run predictions
and ultimately guide the decisions
that brought the astronauts back home safely.
So more than 50 years later,
this idea now has a really great name,
the name of digital twins.
And what's really exciting
is that it's moving well beyond just aerospace engineering.
So in our engineered world,
we're starting to see digital twins of bridges and other civil infrastructure
for structural health monitoring and predictive maintenance.
We're starting to see digital twins of buildings for energy efficiency,
digital twins of wind farms
to increase efficiency and to reduce downtime.
In the natural world,
there's a lot of interest in creating digital twins of forests,
farms, ice sheets, coastal regions, oil reservoirs
and even talk of trying to create a digital twin of planet Earth.
And in the medical world,
there's a great deal of interest in creating digital twins
to help guide medical assessment,
diagnosis, personalized treatment and in silico drug testing.
So, many, many exciting potential applications of digital twins.
But now, I would not like you to leave my talk today
thinking that all of this is a reality,
that we can create digital twins today of all those complex systems.
It's still beyond reach to create a digital twin of an entire aircraft.
It's still beyond reach to create a digital twin of a cancer patient
or of planet Earth.
Creating digital twins of these very, very complex systems
is very, very challenging.
And let's think for a minute why it's so challenging.
So one reason it's very difficult is because of the scales
that these systems cross.
If you think about my aircraft,
damage at the microscopic level
on the material on the wing of the aircraft
translates across scale
to impact the way the vehicle flies at the vehicle level.
In medicine, we all know that, again,
changes at the very fine level,
at the molecular or the cellular level in our bodies
translate across scales to have impacts on us at the system level,
at the human level.
And computational models that resolve all of these scales,
from the microscale all the way up to the system level,
are computationally intractable.
We can't solve them even with today's supercomputing power.
But then you might say, "OK, well what about the data?
You said we had a lot of data.
Can we not just learn digital twins from data?"
So yes, we live in an era of big data
and we have a lot of data often for our systems.
But when it comes to these very challenging,
complex systems in engineering, in science and in medicine,
the data by themselves are almost never enough.
The data are almost always very sparse in both space and in time.
The data are almost always noisy and they're indirect.
As an engineer,
I can almost never measure what it is I want to know.
If I want to know about the health on the structure inside my aircraft wing,
I can't just break it open and take a look.
I am limited to those few sensors that are on the surface of the wing,
taking those measurements and then trying to guess.
More than guess, trying to infer what's happening inside the wing.
The same is true in medicine.
A medical practitioner can't open somebody up to take a look at an organ.
Again, we are limited to sparse,
noisy and indirect observations taken from the outside
to try to infer what's going on.
So then you might say,
"Well, we just have to wait a few years
because sensing technology will get better and better and better."
And that's true.
Maybe, maybe then we'll have enough data
to really be able to characterize
what is going on inside these very complex systems.
But even that's not enough,
because all that would tell us is what's happening now.
And remember, we have to do more than that.
We have to be able to predict what might happen in the future
if we take different actions.
So we're always going to need the models.
So this sounds like a huge challenge, and indeed it is.
But the good news is that we have a lot of hope for addressing this challenge.
And a big part of this hope
rests on this notion of predictive physics-based models.
These are the models that encode the governing laws of nature
that let us make predictions --
predict how a cancer tumor might grow
or how a cancer tumor might respond to radiotherapy treatment,
or predict how an Antarctic ice sheet might flow
under different future temperature scenarios.
And bringing these predictive physics-based models together
with powerful machine learning,
with scalable methods and data simulation
and optimization and decision making,
and with high performance computing,
that's the realm of the interdisciplinary field of computational science,
and that's the focus of the Oden Institute
here at UT Austin,
where we bring together faculty
from 24 different departments across campus
to tackle these kinds of challenging problems.
So I’m going to close by provoking your imagination
and I hope you’re excited, like I am, about the idea of a digital twin.
And maybe as you go home, you can look around and think,
"Oh, what if we had a digital twin of that?"
But let's look at some examples of some of the really exciting areas
where digital twins could make a difference
in tackling some of the biggest problems facing society.
And as I go through this,
you'll also see some of the really exciting research
that we have going on here at UT Austin.
So the first area is space systems.
You probably all know, we are at the dawn of a new space era.
It is so exciting and it's so exciting for our students.
And what's even more exciting is that central Texas
is right in the midst of that new era.
So digital twins clearly have a role to play in managing the health
and the operations of space systems, of launch vehicles, of satellites.
You can see here, this is some of the work
that I'm doing together with my colleagues from the Cockrell School,
Renato Zanetti and Srinivas Bettadpur.
Digital twins also have a big role to play in tracking
and managing space objects and space debris.
And here at UT Austin,
we have one of the world's leading experts in this area,
that's Moriba Jah.
Moriba is building digital twins for space domain awareness.
If we think about the environment and geosciences, again,
digital twins could play such a role here.
This picture you see, Omar Ghattas's, high-resolution,
physics-based model of the Antarctic ice sheet,
which is put together with observational data of all different kinds
to understand what might be going on,
to help guide decisions about where to drill ice cores,
where to take observations,
and ultimately to inform the decision-making around our future climate.
We see also here the work of Clint Dawson
in building a digital twin of a coastal area,
here, the Gulf Coast,
again combining powerful physics models with all the different kinds of data
and here, focused on making storm surge modeling
for hurricanes even more accurate,
again, in support of critical decision-making.
And then in medicine,
I think it's pretty clear that digital twins
have such a role to play
in realizing the promise of personalized medicine.
Here we see some of the work of Michael Sacks
from our Oden Institute Willerson Center,
in moving towards patient-specific,
personalized heart care,
and the work of Tom Yankeelov and David Hellmuth,
also in the Oden Institute,
also working with Dell Medical School and part of biomedical engineering,
in building digital twins for cancer patients.
So I hope that helps to, as I said,
provoke your imaginations to think about what might be possible.
I personally could not be more excited
about a future world where digital twins are enabling safer,
more efficient engineering systems.
They're enabling a better understanding of the natural world around us
and they're enabling better medical outcomes
for all of us as an individual.
Thank you.
(Applause)