Youssef Marzouk, professor of aeronautics and astronautics and co-instructor for the 16,0002/18,0002 course during this time period. Credit score: Gretchen Ertl
An all-new course teaches college students to learn how to use computational strategies to solve real-world problems, from touching down on spaceships to building cell phone towers.
When a Mars lander descends to the floor of Planet Crimson, when can its parachute be safely deployed? Open it too early, while the lander is affected by the environment and it will tear – open too late, however, and the lander may not slow down enough to prevent a catastrophic collision.
There seems to be infinite potential in this complex conundrum.
One strategy to overcome this puzzle is to use a computer to simulate Mars touchdown, which is how college students at 16,0002/18,0002 (Introduction to Computational Science and Engineering) answer this question, which is part of their first set of downsides.
Freshman Andres Arroyo (appropriately) thinks that modeling a Mars landing mission is probably the real-world problem that gets the most attention on the course. Credit score: Gretchen Ertl
“It’s interesting because there are a few ways you can solve problems,” said Andres Arroyo, a freshman who attended the course during the fall. “You can simulate it when it comes to how the speed of the lander adjusts over time, or how the adjustment of speed it regulates takes place. Based on your purpose from the simulation, you can try completely different approaches. I think that’s probably the most attention-grabbing issue we’ve done. ”
The course, which launched last fall, is designed to show college students how to calculate collisions with the physical world. It was developed through MIT The Schwarzman School’s Continuing Computer Training Floor, a multidisciplinary initiative that aims to combine the teaching of computers and disparate disciplines.
The half-semester course places programming in the context of computer science and engineering, an area that focuses on the revolutionary purposes of computation.
Undergraduate students study how to use computer applications to simulate, optimize, and quantify uncertainty. These foundational ideas are framed with tangible examples designed to be relevant to college students who are not essentially computer science majors. Most of the college students on this fall course are studying aeronautics and space travel or math.
Co-instructor Laurent Demanet, professor of leveraged arithmetic. Credit score: Gretchen Ertl
Modeling real life problems
“Simulations like our Mars lander simulator are what people actually use computer systems for. Did NASA fix our small differential equation? Nope, I’m sure they have plenty of additional bells and whistles for their dummies. Conceptually, though, that’s what people really do,” said Youssef Marzouk, professor of aeronautics and astronautics and co-instructor for this time period course. “That’s how I work, even in my own analysis. There’s the modeling, there’s the code, there’s the output of the code, and you can iterate between these too. “
Building around the course such concrete examples provides undergraduates with a way of the number of problems that can be approached using computational fashion. Most college students take the course in their first or second 12 months, and there are many but to choose the important one, so it is especially helpful to give them a style of calculation. used in many fields, he said.
Ultimately, the abilities college students build on this course will help them deal with problems of scientific prediction in whatever form of self-realization they choose, Demanet says. Credit score: Gretchen Ertl
Co-instructor Laurent Demanet, professor of manual arithmetic who designed the course with David, in order to develop the course, the school needed to collect the fundamentals of computational science and engineering in a way bring ideas to life for college students. Darmofal, Jerome C. Hunsaker Professor of Aeronautics and Astronautics.
Lectures bring together basic equations at work in a certain way, comparable to Newton’s laws of motion for the case of the Mars lander, which university students then study to materialize these key equations in an algorithm.
“It’s a mix of math with computer science and computer science. It sings while you put it all together,” says Demanet. “For academics, it’s really a skill-based class. We need to provide university students with specialized knowledge that can be used almost everywhere in their later studies, then in a variety of niches. ”
From equation to simulation
During a lecture, Demanet described Newton’s law of cooling (the rate at which an object cools is proportional to the difference in temperature between the object and its environment). He then ran a simulation using Python code to confirm how long it would take for a cup of cold espresso from 85 to 50 levels.
One of the biggest challenges of developing the course, he said, was to introduce these mathematical ideas, while giving college students enough context for them to make sense for a number of purposes. updates – but overwhelms them with too many details.
In the past conveying specific expertise, these examples were additionally designed to encourage college students. For example, a lecture focused on local weather science used mathematical equations to convert warmth to disprove a false claim that water vapor is a more potent greenhouse gas than carbon dioxide.
However Demanet advises scholars not to use his phrase for it – he has demonstrated a computer simulation that confirms how greenhouse gases have influenced the overall increase in world temperatures over many years.
Outside of the classroom, college students have used their computational methods for a limited variety of real-world units, from optimizing the placement of cell phone towers around MIT, to finding chart how the effectiveness of the Covid-19 vaccine declines over time, to assess the effect geothermal heating can have on the temperature inside a home.
For Penelope Herrero-Marques, the geothermal case piqued her curiosity because at some point she wanted to put in place a system in her own home to cut her carbon footprint. Herrero-Marques, a second-year mechanical engineering student who took her final course in the spring, was intrigued by its associated downsides unit despite her little knowledge. Basic knowledge of how to use computational methods.
“Some problems are a little scary at first simply because they are so big. For our first p-set in the classroom, we imagine simulating a Mars crash. However, the professors did an excellent job breaking it down into smaller problems. Don’t get overwhelmed. Every major downside, she says, can be broken down into smaller problems that you’re actually likely to have to deal with.
She is currently sharing those insights as an assistant instructor for the course.
Assistant instructor Mark Chiriac, a sophomore, took the course in its first iteration. The math major needed to learn more about algorithms but also focused on the purposes he discovered to attract attention, like planetary motion.
While one of the most convoluted issues involves finding cell phone towers around MIT, it’s also among Chiriac’s favorites because the example is so real. He says that effectively overcoming that optimization drawback has given him the audacity to use these expertise in different programs.
“This course brings together elements of coding, math, and physics into this lovely combination to give people the tools to solve very relevant problems that can be so important in the world of computers. us right now. It confirmed to me how these completely disparate fields of science come together in ways that I know exist, yet have no skill on their own,” he said.
Ultimately, the abilities college students build on this course will help them deal with problems of scientific prediction in whatever form of self-realization they choose, Demanet says.
“I hope scholars go away with an appreciation of how computation can be used to essentially simulate the difficult problems on earth around them,” Marzouk provides. “I hope they see the possibilities it has and have some appreciation that it’s not just a black field. There are really interesting concepts and algorithms that go into how that happens. Whether they spend the rest of their careers researching these concepts and algorithms or whether they stop working right here, I believe it can be a useful lesson. ”
