What is Computational Science SCI PD 3

Summary

This video explores computational science, a field merging computer science, mathematics, and traditional science, to model and simulate complex real-world problems. Highlighting its role as a complement to theoretical and experimental science, the script discusses the computational science cycle, from problem selection to simulation and data analysis. It showcases applications in studying ant colonies for network design and public safety, including emergency evacuation planning. The video also introduces tangible computing, demonstrating interactive simulations for practical problem-solving in areas like wildfire management.

Takeaways

• 🌐 Computational science is a new branch of science that combines computer science, mathematics, and science to model and simulate real-world problems.
• 🔬 It acts as the 'third leg' of science, complementing theoretical and experimental science with its ability to analyze large datasets and conduct simulations.
• 💡 The advent of powerful computers has enabled computational science to design and conduct experiments on complex systems that are impractical to study in the real world.
• 🔄 Computational science allows for the running of multiple 'what-if' scenarios quickly, aiding in the understanding and prediction of real-world phenomena.
• 🤖 It does not replace traditional experimentation but is used in conjunction to provide a more comprehensive approach to scientific inquiry.
• 🔍 The computational science cycle involves selecting a real-world problem, abstracting it into a model, translating the model into algorithms, and then running simulations to test the model.
• 🐜 Melanie Moses, a computer scientist and biologist, uses agent-based models to study ant colonies and apply insights to computer networks and swarm robotics.
• 🔧 By using genetic algorithms, researchers can evolve the parameters of models to find optimal behaviors, such as efficient foraging strategies in ant colonies.
• 🤖 Swarm robotics utilizes strategies learned from biological systems, like ants, to create scalable distributed search mechanisms in robotic systems.
• 🚒 Stephen Guerin uses computational science for public safety issues, including emergency evacuation planning and simulating the spread of fires.
• 📊 The application of computational science in real-world scenarios involves tangible computing, where physical interactions with a surface can enhance learning and understanding of complex systems.
• 🌐 Computational science integrates computational thinking into traditional scientific methods, helping to address complex global challenges such as climate change, biodiversity loss, energy consumption, and epidemics.

Q & A

What is computational science as described in the video?

Computational science is a new type of science made possible by computers, which lies at the intersection of computer science, mathematics, and traditional science. It uses mathematics and computer science to model real-world problems and conduct simulations, serving as the third leg of science in addition to theoretical and experimental science.

How does computational science impact everyday life?

Computational science impacts everyday life by enabling the design and conduct of experiments on complex systems that are too big, too expensive, or too dangerous to experiment with in the real world. It allows for running multiple what-if scenarios quickly and analyzing large amounts of data, which can lead to new insights and solutions to various problems.

What is the computational science cycle as mentioned in the video?

The computational science cycle is a process used by computational scientists that includes: selecting a real-world problem or phenomenon, creating an abstraction or model, translating the model into a computational representation using mathematics and algorithms, translating algorithms into computer code, and finally running simulations to test the model.

How does Melanie Moses use computational science in her research?

Melanie Moses, a computer scientist and biologist, uses computational science to study complex systems like ant colonies and computer networks. She uses agent-based models to simulate ant colonies, applies genetic algorithms to evolve model parameters, and then applies the strategies learned from ants to swarm robotics, demonstrating the scalability and efficiency of distributed search mechanisms.

What is the significance of using genetic algorithms in Melanie Moses' research?

Genetic algorithms are used to evolve the parameters of the model in Melanie Moses' research, allowing for the exploration of many different behavioral patterns of the ants. The goal is to find the optimal strategy that maximizes the rate of seed collection, providing insights into effective foraging strategies.

How are swarm robotics applied in Melanie Moses' work?

In Melanie Moses' work, swarm robotics is applied by programming the strategies learned from ant colonies into robots. These robots, controlled by iPhones and equipped with simple motors and sensors, are used to demonstrate collective foraging behaviors based on the strategies that were effective for the ants.

What is Stephen Guerin's role in computational science?

Stephen Guerin is a computational scientist who uses modeling and simulation to address public safety issues such as emergency evacuation planning. He works with applied complexity companies to find real-world applications for complex systems research.

What are some of the tools Stephen Guerin uses in his work?

Stephen Guerin uses tools like agent-based modeling, machine learning, machine vision, statistics, and probability in his work. These tools are integrated into interactive physical tables to examine emergency management issues, such as fire spread and evacuation dynamics.

Can you explain the concept of tangible computing as shown in Stephen Guerin's demonstration?

Tangible computing, as demonstrated by Stephen Guerin, allows users to interact with a physical surface to form and manipulate data. It combines physical interaction with digital information, enabling users to learn and understand complex systems more intuitively, such as forming topographical maps with their hands.

How does the interactive table technology work in Stephen Guerin's demonstration?

The interactive table technology uses a projector to display information onto a surface, a webcam to capture interactions with that surface, and a computer to process the data. It allows for the detection of laser pointers or hand movements, mapping them onto the projected surface to create an interactive experience for analyzing complex systems.

What is the importance of computational science in addressing complex problems like climate change and epidemics?

Computational science is important in addressing complex problems because it allows scientists to create detailed models and simulations that can predict outcomes and test strategies. This can help in understanding, predicting, and potentially preventing or mitigating the impacts of issues like climate change, loss of biodiversity, energy consumption, and epidemics.

Outlines

00:00

🔬 Introduction to Computational Science

Maureen Saladombrowski introduces the concept of computational science as a third leg of science, complementing theoretical and experimental science. It sits at the crossroads of computer science, mathematics, and traditional science, utilizing these disciplines to model and simulate real-world problems. The advent of powerful computers has facilitated the exploration of complex systems that are impractical or hazardous to study directly. Computational science enables running numerous 'what-if' scenarios and analyzing vast datasets, but it is not a substitute for traditional experimentation. The computational science cycle is outlined, starting from identifying a real-world problem, creating an abstraction, translating it into a computational model, and running simulations to draw conclusions about the model's realism and predictive power.

05:03

🐜 Studying Ant Colonies and Swarm Robotics

Melanie Moses, a computer scientist and biologist, discusses her research using agent-based models to study complex systems, specifically ant colonies and computer networks. Her lab has used genetic algorithms to evolve model parameters, optimizing the rate of seed collection by simulating ant foraging behaviors. The strategies derived from these models were then applied to swarm robotics, demonstrating the scalability of distributed search mechanisms. The research highlights the potential for computer science to learn from biological systems to address complex challenges, such as the design of distributed strategies in swarm robotics.

10:04

🚒 Public Safety and Emergency Management Simulations

Stephen Guerin, a computational scientist, explains his work on modeling and simulation to address public safety issues like emergency evacuation planning. He uses tools like agent-based modeling, machine learning, and machine vision to create interactive simulations. The technology allows for the projection of complex systems onto physical surfaces, making them interactive for analysis. Guerin demonstrates tangible computing, where physical terrain can be manipulated to represent real-world scenarios like fire spread and evacuation dynamics. The system integrates GIS data, fuel types, and wind vectors to simulate fire behavior and human responses, providing a hands-on approach to complex problem-solving in emergency management.

15:06

🌐 The Impact of Computational Science on Problem Solving

The final paragraph emphasizes the role of computational science as a new branch of science that integrates computational thinking and computing into various scientific disciplines. It highlights how scientists are leveraging computer modeling and simulation to understand, predict, and prevent significant global challenges such as climate change, biodiversity loss, energy consumption, and epidemics. The paragraph underscores the innovative and interdisciplinary nature of computational science and its potential to contribute to solving complex real-world problems.

Keywords

💡Computational Science

Computational Science is an interdisciplinary field that combines computer science, mathematics, and domain-specific knowledge to model and analyze complex systems. It is defined as the third leg of science, complementing theoretical and experimental approaches. In the video, it is portrayed as a key enabler for understanding and solving real-world problems that are too large, costly, or dangerous to tackle through traditional methods. The script discusses how computational science uses simulations and data analysis to predict outcomes and inform decision-making.

💡Agent-Based Modeling

Agent-Based Modeling (ABM) is a computational method used to simulate the actions and interactions of autonomous agents, whether they are individuals, animals, or robots, in order to understand the system as a whole. In the script, Melanie Moses uses ABM to study ant colonies and computer networks, highlighting how insights from one system can inform the optimization of the other, demonstrating the cross-disciplinary utility of ABM.

💡Simulation

A simulation in the context of the video refers to the process of running a model to mimic the operation of a real-world process or system over time. It is a tool used in computational science to predict future scenarios and analyze complex systems. The script mentions simulations being used to study ant colony behaviors and to predict the spread of fire in emergency evacuation planning.

💡Model

In the video, a model is an abstract representation of a real-world system or phenomenon. It is used in computational science to simplify and capture the essential features of the system for analysis. The script describes the process of creating a computational model by translating abstract ideas into formal mathematics and algorithms, which can then be used in simulations.

💡Genetic Algorithms

Genetic Algorithms are a subset of evolutionary algorithms that use mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection, to generate solutions to optimization and search problems. In the script, they are used by Melanie Moses to evolve the parameters of her ant colony model, finding the most effective foraging strategies.

💡Swarm Robotics

Swarm Robotics refers to a system of robots that coordinate and communicate with each other to achieve a common goal. The script describes how the strategies learned from ant colonies are applied to swarm robotics, with robots programmed to forage using behaviors derived from the study of ants, demonstrating the practical application of computational models.

💡Machine Learning

Machine Learning is a field of artificial intelligence that involves the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions, relying on patterns and inference. In the video, it is part of the toolset used for addressing complex systems and is integral to the interactive table technology for emergency management simulations.

💡Machine Vision

Machine Vision is a branch of computer vision that enables machines to provide meaning to the visual world by interpreting and understanding images and scenes. In the script, it is used to make interactive surfaces for emergency management simulations, detecting the position of a laser pointer or hand gestures to interact with projected images.

💡Tangible Computing

Tangible Computing is an approach that allows users to interact with digital information through physical objects and actions in the real world. The script describes how tangible computing is used in the interactive table setup, enabling users to manipulate physical sand to form terrain models that can be analyzed and used in simulations.

💡GIS Information

GIS, or Geographic Information System, is a framework used to capture, analyze, and manage spatial and geographical data. In the video, GIS information is used to project and interact with geographical data on the interactive table, allowing for the simulation of scenarios like fire spread and evacuation planning based on real-world topography and environmental factors.

💡Cellular Automata

Cellular Automata are computational models used to simulate complex systems based on a grid of cells that follow simple rules of interaction with their neighbors. In the script, a biased cellular automata model is used to simulate the spread of fire, taking into account factors like wind direction, slope, and fuel type.

Highlights

Introduction to computational science as the third leg of science alongside theoretical and experimental science.

The role of computational power in enabling complex models and simulations that were previously infeasible.

Computational science's ability to conduct multiple what-if scenarios and analyze large data sets.

The distinction between computational science and traditional field experimentation, emphasizing their respective appropriateness.

Description of the computational science cycle, from selecting a real-world problem to running simulations.

Melanie Moses' research using agent-based models to study ant colonies and computer networks.

Application of genetic algorithms to evolve model parameters for optimal foraging behavior in ant simulations.

Translating ant foraging strategies into swarm robotics, demonstrating the practical application of computational biology.

Stephen Guerin's work on public safety issues using computational modeling and simulation.

Innovative use of interactive tables for emergency management planning, combining physical interaction with digital data.

Technological setup for interactive tables, including projectors, cameras, and machine vision algorithms.

Tangible computing's role in enhancing learning and interaction with complex systems.

Demonstration of 3D scanning and terrain manipulation for emergency response training.

Integration of GIS data for detailed environmental and emergency response simulations.

Simulation of fire spread dynamics and the strategic placement of firefighting resources.

Human-computer interaction in the context of emergency evacuation planning and traffic dynamics.

The potential of computational science to address complex global challenges such as climate change and energy consumption.

Transcripts

hi and welcome everyone my name is

maureen saladombrowski and i work with

project guts in santa fe new mexico

in this video we're going to explore

computational science a new type of

science made possible by computers and

the impact it's having on our everyday

lives

computational science can be seen as the

third leg of science in addition to

theoretical and experimental science

it lies at the intersection of computer

science mathematics and science

computational science uses mathematics

and computer science to model real-world

problems and conduct simulation

experiments

computational science is made possible

by the advent of powerful computers

increases in computational power have

enabled us to design and conduct

experiments on models of systems that

are too big too expensive or too

dangerous to experiment with in the real

world increased computational power

allows us to run multiple what-if

scenarios very quickly

we also collect and analyze large

amounts of data produced by these models

but it is important to note that

computational science does not replace

traditional field

experimentation each approach is

appropriate in different situations

computational science opens up new

opportunities for problem solving and

empowering students as scientists

we use the computational science cycle

to describe the process used by

computational scientists

we start by selecting a real world

problem or phenomenon we're interested

in studying

then we need to make a simplified

version of the real world doing so

produces an abstraction for a model

next we go from the abstract idea for a

model to a computational model by

representing the components and

behaviors in terms of formal mathematics

and algorithms

the next step is to translate the

algorithms into a computer code

these four steps are called computer

modeling

finally we run simulations using the

computer model we created as an

experimental testbed

simulations run time forward as if we

could speed up time to see how the

future unfolds

during the simulation we can produce and

capture data from these data we draw

conclusions and interpret if our model

has any basis in reality

if the model reproduces some features of

reality that we care about as compared

to the real world data perhaps it can be

used to help us understand or make

predictions about the real world

scientists and researchers use computer

models to study a wide variety of

phenomenon

let's hear from melanie moses melanie is

a computer scientist and biologist she

uses computer simulations of ant

colonies to study and design computer

networks

a professor in the department of

computer science here at unm and i also

have an appointment in the department of

biology

and i'm going to tell you about some

research that we've been doing in my lab

over the last couple of years

using agent-based models very much like

the kinds of agent-based models that you

all are learning to build

and we've used those models to study

complex systems

and

the systems we focus on are ant colonies

and computer systems and we learn a lot

about

each system by studying the other so in

our models foragers are searching for

food on a grid

and upon finding this food they decide

whether or not to return to where they

went use inside fidelity or to

communicate using pheromones

and we used a technique called genetic

algorithms to evolve the parameters of

the model so these

in other words are many different ways

that the ants could behave many

different ways they could move many

different ways they could balance memory

and communication and our goal was to

find the way that maximized the rate

that seeds were collected in a fixed

period of time

so we learned a great deal about what we

think the ants in the field are doing

and what good strategies for foraging

collectively are so we then wanted to

take those strategies and do something

with them this was going to be sort of

our final test about whether the

strategies really worked and so what we

did is we took those strategies and we

programmed them

we use them as the programs that govern

swarms of robots so we built these

robots in our labs these are robots

controlled by iphones there's an iphone

up here and a pretty simple motor and a

few sensors

and these robots then we send them out

collectively we have a group of six of

them and we send them out to forage

using the behaviors that the ants have

told us are good foraging behaviors what

i told you about is um

you know kind of my belief that these

computational and biological systems are

both these kind of complex systems where

you have interacting agents that are

hooked together by networks of

communication

and

we can use computer science we can use

models to reveal how biology

biological complex systems work

and on the other hand we can go to

biology and we can ask how do

distributed strategies work in

particular these ants

taught us that

you can have a scalable distributed

search mechanism by balancing individual

memory uh with pheromone communication

and we were able to take that and put

that into swarm robotics so this is a

case where we have

mobile computers moving around

interacting with each other that can now

imitate the way that ants

communicate with each other to achieve

some task

and i think this sort of approach

um is important because as computer

science

as computers become more and more

internetworked their interactions with

humans with each other with the physical

world all um introduce this new layer of

complexity

and i think that biology has evolved

many interesting solutions to these

these sorts of complex

challenges and so i'm hopeful that we

actually will learn a great deal more

about how to build computer systems by

studying biological systems

stephen guerin is a computational

scientist who uses modeling and

simulation to address public safety

issues such as emergency evacuation

planning

hi my name is steven guerin i'm working

here in santa fe at a company called

redfish and another one called sim table

where we're an applied complexity

company which is where we're taking

ideas coming out of places like santa fe

institute unm los alamos and sandia

looking at complex systems and finding

applications to them in the real world

some of the tools that we're using are

things like agent-based modeling which

you're learning in

tools like netlogo machine learning

machine vision

statistics and probability are kind of

you come together into

ways that people can come around a

physical interactive table to look at

emergency management issues like how

will a fire spread how will fluid move

down in a dam break how what's the

social side of an evacuation who's going

to shelter in place who might evacuate

and also the traffic dynamics

arising from these instances so instead

of traditionally presenting our results

up on a screen

or on a wall

either on the laptop or on the wall

we're taking the same projector and

projecting it down onto surfaces or

around the room and then making those

surfaces interactive by watching that

same surface with a camera so this

has nice challenges of how do you how do

you detect where a laser pointer is

clicking on a very non-uniform surface

and map that up to a projector or how do

i detect where somebody's hand is or

their their body is so these are all

nice problems in machine vision

at the core of i think computer science

today and many applications

in addition with simulation

big data and analytics as well as

machine learning

so what we have here

up on the top maybe out of frame as we

pan over here is we have a projector

that is just an off-the-shelf projector

projecting on a table we have a web

camera and a mac mini bolted on the back

and that's the full extent of the

computation that's going on and we're

projecting that down onto a table here

and

we'll turn off the lights here so you

can get a better view of it

and

basically you're seeing the camera

taking a picture of this

table and reflecting these white uh

borders back so i don't know

if you can see my hands in here or the

laser pointer and in the beginning you

can see there's a little bit uh well

first of all the camera is upside down

flipped and it's a wide angle so the

image is a little bowed so the first

algorithm we're going to do

is we're going to take

the projector space and convert the x

and y of the projectors into a binary

code

and there's a particular kind of binary

code called gray code

that we're going to now come through and

project that great code of all the

position coordinates and let the camera

take a photo

and it's learning for every pixel in the

camera it's positioned in camera space

and then converting that to a projector

space

so once the the camera registers that

we're able to bring up gis information

right now the table is flat

and we're projecting

the area of santa fe here for instance

and we also have the ability to use my

laser pointer and make the surface

interactive

the first thing i'm going to do is i'm

going to put it into 3d scan mode

and i'm going to make some arbitrary

hills

so imagine a firefighter wanted to train

on how fire behaves when it's going

through a valley or through a saddle

point

and

so we make uh we have the ability now to

project some lines on the table and this

is a sinusoidal grayscale pattern and

based on how the stripes move and the

displacement of the camera from the

projector there's enough information to

recover the height of the sand so that

scanning process

lets us now use that as a real

information in a fire

the other way we like to use this is

loading a known topography like in santa

fe

and let me turn off all these different

layers for you first

and we start off with the colors of the

rainbow

and we can also click on any one

location in here and fly to that

position in google earth so now we're

registered in gis space

so what i'm going to do now with the

colors of the rainbow

is i'm going to move the sand

from the low points kind of the red

points

and using the colors of the rainbow roy

g biv red orange yellow green blue

indigo and violet we're going to make

the terrain of santa fe

and i'll

take my trusty

piece of wood here

and get the bulk of the sand to the east

in santa fe in the east mountains

this is north on the table

i'm just going to bring the sand in here

roughly

and then we'll do a little finer detail

with the hands

so this is um

so right now we're forming the ski basin

here's the santa fe uh watershed coming

down here with the

the mcclure nichols reservoir cerro

gordo

this is hyde park coming through here up

to the ski basin

and this is thompson peak in the east

and so part of this is we call tangible

computing

uh and it lets people kind of interact

with a real surface and and actually

form the surface and get a little bit of

muscle memory as people learn in

different ways some people can just look

at a contour map like an expert but some

people

learn contours in a different way in

elevation and being able to form it with

their hands

has some advantage

so this is a roughly santa fe

with the mountains in the east

and now we can layer on different pieces

of information

here we're going to show

i'll turn on hill shading so if you

think of a a raster or bitmap with

elevations

we can look at every point on that patch

like for your net logo and look at its

eight neighbors and figure out what

direction that patch is uh facing we

call that aspect and gis and then we can

color that or shade it based on where

the sun is so here i'll move the sun to

the east or the west

and we can put a little bit more detail

on here

the elevation data was coming from the

usgs at a 10 meter resolution

per pixel

we can now lay around things like the

roads as a polyline

or as a structures which are points so

these are the houses

and we can also come in and inspect

certain areas

so i can say well what is the fuel or

vegetation type in any one of these

pixels so it's it's like i'm inspecting

a patch and it's a patch variable and

that ultimately we're going to have a

fire model on here that wants to move

uphill

downwind it'll be a function of the fuel

type as well as the strength and

direction of the wind which is a single

vector here with the strength and

direction of the wind indicated

so once we have this in here

while i was inspecting the patch layer

and we're showing elevation but i can

actually show the fuels layer also so

here's

your ponderosa pine pinion juniper and

grass and chemisa and so once we have

this loaded i've got all the features

necessary to light a fire and having a

biased cellular automata model of how

fire spreads so let's put this guy into

fire mode

and we can start up a fire maybe down uh

near uh

this kind of upper canyon and cerro

gordo kind of intersecting here and if

you can see it's maybe easier to see on

the terrain view here

so we have a fire spread now that's

a function of the direction of the wind

and the slope and i can speed up time

and we'll watch that thing spread or we

can also simulate what if there was

spotting behavior up on the hills as the

wind is pushing it

and we can also think about the human

response of

where would i put maybe an air tanker

to slow down the head of the fire the

direction in which it's going which is a

very dangerous place to put human

resources so we want to use our airplane

to slow down the fire there

and we maybe put our humans uh with a

hand crew

at the heel or the base of the fire away

from you know downhill

and down wind or upwind of the fire

excuse me

and these guys will have a certain

production rate

be easier to see if i turn off the roads

here

and so these guys are making their line

in a certain rate

we can also then introduce things like a

bulldozer team who might be a resource

of arriving later

as the fire gets more progressed but you

can you know compare compare their

progression rates to the hand crews

over time so they're able to dig a lot

more line to contain this fire

so this is the physical aspect of a fire

we can also turn on the roads and the

structures and for every house

we can simulate an evacuee or one in

this case one and a half evacuees per

house

and start to look at where we'd expect

congestion to be so now we can have the

fire service interacting with

public safety or the police who are

going to be in charge of the evacuation

typically these guys train separately on

their part of the problem this lets them

come together around a common problem

and deal with those issues so this is a

first instance of using agent-based

modeling in the real world

kind of a new form of human computer

interaction that takes advantage of

machine learning simulation

and um in a lot of statistics so

think of this as new ways of

of solving problems this is sim table

as we've seen computational science is a

new branch of science that integrates

computational thinking and computing

into the sciences

scientists are using computer modeling

and simulation to understand predict and

prevent the daunting problems we face

such as climate change loss of

biodiversity energy consumption and

epidemics

you