Welcome! My name is Maureen Saladombrowski, and I am part of Project GUTS in Santa Fe, New Mexico. Today, we will delve into the fascinating world of computational science, a revolutionary field enabled by computers that is transforming our daily lives.
Computational science is often considered the third pillar of science, complementing theoretical and experimental science. It exists at the crossroads of computer science, mathematics, and traditional sciences. By leveraging mathematics and computer science, computational science allows us to model complex real-world problems and conduct simulation experiments.
The rise of powerful computers has made this field possible. With increased computational power, we can design and execute experiments on models of systems that are too large, costly, or hazardous to test in reality. This capability enables us to quickly run numerous “what-if” scenarios and analyze vast datasets generated by these models. However, it’s crucial to understand that computational science does not replace traditional field experiments; each method is suitable for different contexts.
Computational science offers new avenues for problem-solving and empowers learners to become scientists. The process used by computational scientists is known as the computational science cycle. It begins with selecting a real-world problem or phenomenon of interest. We then create a simplified version of reality, forming an abstraction for a model. This abstract idea is translated into a computational model by representing its components and behaviors through formal mathematics and algorithms. Finally, simulations are run using the computer model as an experimental testbed.
During simulations, data is generated and captured, allowing us to draw conclusions and assess whether our model reflects reality. If the model successfully reproduces certain aspects of reality, it can help us understand or predict real-world phenomena.
Researchers use computer models to explore a wide range of phenomena. Let’s hear from Melanie Moses, a computer scientist and biologist, who uses computer simulations of ant colonies to study and design computer networks.
“As a professor in the Department of Computer Science and the Department of Biology, I have been conducting research using agent-based models. These models help us study complex systems, focusing on ant colonies and computer systems. In our models, foragers search for food on a grid, and upon finding food, they decide whether to return or communicate using pheromones. We employed genetic algorithms to evolve the model’s parameters, exploring various behaviors and foraging strategies. This research provided insights into ant behavior and effective collective foraging strategies.
We then programmed these strategies into swarms of robots in our labs. Controlled by iPhones, these robots collectively forage using the identified effective behaviors. This approach demonstrates how both computational and biological systems are complex systems with interacting agents connected by communication networks. By studying biological systems, we can learn to build better computer systems.”
Next, we hear from Stephen Guerin, a computational scientist who uses modeling and simulation to address public safety issues, such as emergency evacuation planning.
“At Redfish and SimTable in Santa Fe, we apply complexity science to real-world applications. We use tools like agent-based modeling, machine learning, and machine vision to tackle emergency management issues, such as fire spread and evacuation management. Instead of displaying results on a screen, we project them onto surfaces, making them interactive. This involves challenges in machine vision and interaction design.
For instance, we can create a 3D scan of a terrain and simulate fire behavior in various scenarios. By layering information such as elevation and vegetation types, we can model fire spread and analyze human responses to emergencies. This allows us to explore innovative solutions to complex problems through human-computer interaction.”
As demonstrated, computational science is a burgeoning branch of science that integrates computational thinking and computing into traditional sciences. Scientists are utilizing computer modeling and simulation to understand, predict, and address significant challenges such as climate change, biodiversity loss, energy consumption, and epidemics.
Choose a real-world problem that interests you, such as climate change or traffic flow. Work in groups to create a simplified model of this problem. Discuss the key components and behaviors that need to be represented in your model. Use formal mathematics and algorithms to translate your abstraction into a computational model. Present your model to the class and explain your approach.
Using a computational tool or software, run simulations based on the model you created in the previous activity. Capture the data generated during these simulations. Analyze the results and determine whether your model accurately reflects the real-world phenomenon. Prepare a report discussing your findings and any potential improvements to your model.
Research agent-based modeling and its applications in computational science. Create a simple agent-based model using a platform like NetLogo. Simulate a scenario, such as predator-prey interactions or crowd dynamics. Observe the emergent behaviors and discuss how agent-based models can provide insights into complex systems.
Read about a case study where computational science was used to solve a real-world problem, such as emergency evacuation planning or ant colony behavior. Analyze the methodologies and tools used in the study. Discuss in groups how computational science contributed to understanding or solving the problem and what challenges were encountered.
In groups, brainstorm potential future applications of computational science in various fields such as healthcare, environmental science, or urban planning. Consider emerging technologies and how they might enhance computational modeling and simulation. Present your ideas to the class, highlighting the potential impact and challenges of these applications.
Sure! Here’s a sanitized version of the provided transcript:
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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.
The advent of powerful computers has made computational science possible. 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. This increased power allows us to run multiple “what-if” scenarios very quickly and analyze large amounts of data produced by these models. However, it’s 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 empowers 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 create a simplified version of the real world, producing an abstraction for a model. Next, we translate the abstract idea into a computational model by representing the components and behaviors in terms of formal mathematics and algorithms. Finally, we run simulations using the computer model we created as an experimental testbed.
During the simulation, we can produce and capture data, from which we draw conclusions and interpret whether our model has any basis in reality. If the model reproduces some features of reality that we care about, it may help us understand or make predictions about the real world.
Scientists and researchers use computer models to study a wide variety of phenomena. Let’s hear from Melanie Moses, a computer scientist and biologist who uses computer simulations of ant colonies to study and design computer networks.
Melanie: “I’m a professor in the Department of Computer Science and also have an appointment in the Department of Biology. I’m going to tell you about some research we’ve been doing in my lab over the last couple of years using agent-based models, similar to the kinds of models you’re learning to build. We’ve used those models to study complex systems, focusing on ant colonies and computer systems. In our models, foragers search for food on a grid, and upon finding food, they decide whether to return or communicate using pheromones. We used genetic algorithms to evolve the parameters of the model, exploring different behaviors and strategies for foraging. We learned a lot about what we think ants in the field are doing and what good strategies for collective foraging are.
We then programmed these strategies into swarms of robots we built in our labs. These robots are controlled by iPhones and are sent out collectively to forage using the behaviors identified as effective. This approach highlights how computational and biological systems are both complex systems with interacting agents connected by networks of communication. By studying biological systems, we can learn how to build better computer systems.”
Next, we hear from Stephen Guerin, a computational scientist who uses modeling and simulation to address public safety issues, such as emergency evacuation planning.
Stephen: “I’m working in Santa Fe at a company called Redfish and another called SimTable, where we apply complexity science to real-world applications. We use tools like agent-based modeling, machine learning, and machine vision to address emergency management issues, such as how a fire spreads or how to manage evacuations. Instead of presenting results on a screen, we project them onto surfaces, making them interactive. This involves challenges in machine vision and interaction design.
For example, we can create a 3D scan of a terrain and simulate how fire behaves in different scenarios. By layering information such as elevation and vegetation types, we can model fire spread and analyze the human response to emergencies. This allows us to explore new ways of solving complex problems through human-computer interaction.”
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 daunting problems such as climate change, loss of biodiversity, energy consumption, and epidemics.
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This version maintains the core content while removing any informal language and ensuring clarity.
Computational – Relating to the process of mathematical calculation, especially using computers. – The computational power of modern processors allows for complex simulations in physics.
Science – The systematic study of the structure and behavior of the physical and natural world through observation and experiment. – Advances in computer science have revolutionized the way we process and analyze data.
Modeling – The creation of a representation of a system or process to analyze and predict its behavior. – In computer science, modeling is essential for developing accurate simulations of real-world phenomena.
Simulation – The imitation of the operation of a real-world process or system over time. – The simulation of weather patterns helps scientists predict future climate changes.
Algorithms – A set of rules or processes to be followed in calculations or problem-solving operations, especially by a computer. – Efficient algorithms are crucial for processing large datasets in a reasonable time frame.
Datasets – Collections of data, often used for analysis and research purposes. – Researchers rely on large datasets to train machine learning models effectively.
Research – The systematic investigation into and study of materials and sources to establish facts and reach new conclusions. – Conducting research in artificial intelligence can lead to groundbreaking innovations.
Systems – Complex networks of components that work together to perform a specific function. – Operating systems manage the hardware and software resources of a computer.
Behaviors – The actions or reactions of an object or system in response to external or internal stimuli. – Understanding the behaviors of algorithms under different conditions is crucial for software development.
Networks – Interconnected systems or groups of interconnected computers or devices. – Computer networks enable the sharing of resources and information across multiple devices.
