Category Archives: Computer

CAD designs in real time

Almost every object we use is developed with computer-aided design (CAD). Ironically, while CAD programs are good for creating designs, using them is actually very difficult and time-consuming if you’re trying to improve an existing design to make the most optimal product.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Columbia University are trying to make the process faster and easier: In a new paper, they’ve developed InstantCAD, a tool that lets designers interactively edit, improve, and optimize CAD models using a more streamlined and intuitive workflow.

InstantCAD integrates seamlessly with existing CAD programs as a plug-in, meaning that designers don’t have to learn new tools to use it.

“From more ergonomic desks to higher-performance cars, this is really about creating better products in less time,” says Department of Electrical Engineering and Computer Science PhD student and lead author Adriana Schulz, who will be presenting the paper at this month’s SIGGRAPH computer-graphics conference in Los Angeles. “We think this could be a real game changer for automakers and other companies that want to be able to test and improve complex designs in a matter of seconds to minutes, instead of hours to days.”

The paper was co-written by Associate Professor Wojciech Matusik, PhD student Jie Xu, and postdoc Bo Zhu of CSAIL, as well as Associate Professor Eitan Grinspun and Assistant Professor Changxi Zheng of Columbia University.

Traditional CAD systems are “parametric,” which means that when engineers design models, they can change properties like shape and size (“parameters”) based on different priorities. For example, when designing a wind turbine you might have to make trade-offs between how much airflow you can get versus how much energy it will generate.

However, it can be difficult to determine the absolute best design for what you want your object to do, because there are many different options for modifying the design. On top of that, the process is time-consuming because changing a single property means having to wait to regenerate the new design, run a simulation, see the result, and then figure out what to do next.

With InstantCAD, the process of improving and optimizing the design can be done in real-time, saving engineers days or weeks. After an object is designed in a commercial CAD program, it is sent to a cloud platform where multiple geometric evaluations and simulations are run at the same time.

With this precomputed data, you can instantly improve and optimize the design in two ways. With “interactive exploration,” a user interface provides real-time feedback on how design changes will affect performance, like how the shape of a plane wing impacts air pressure distribution. With “automatic optimization,” you simply tell the system to give you a design with specific characteristics, like a drone that’s as lightweight as possible while still being able to carry the maximum amount of weight.

The reason it’s hard to optimize an object’s design is because of the massive size of the design space (the number of possible design options).

“It’s too data-intensive to compute every single point, so we have to come up with a way to predict any point in this space from just a small number of sampled data points,” says Schulz. “This is called ‘interpolation,’ and our key technical contribution is a new algorithm we developed to take these samples and estimate points in the space.”

Matusik says InstantCAD could be particularly helpful for more intricate designs for objects like cars, planes, and robots, particularly for industries like car manufacturing that care a lot about squeezing every little bit of performance out of a product.

“Our system doesn’t just save you time for changing designs, but has the potential to dramatically improve the quality of the products themselves,” says Matusik. “The more complex your design gets, the more important this kind of a tool can be.”

The design of industrial processes for drug manufacturing

When organic chemists identify a useful chemical compound — a new drug, for instance — it’s up to chemical engineers to determine how to mass-produce it.

There could be 100 different sequences of reactions that yield the same end product. But some of them use cheaper reagents and lower temperatures than others, and perhaps most importantly, some are much easier to run continuously, with technicians occasionally topping up reagents in different reaction chambers.

Historically, determining the most efficient and cost-effective way to produce a given molecule has been as much art as science. But MIT researchers are trying to put this process on a more secure empirical footing, with a computer system that’s trained on thousands of examples of experimental reactions and that learns to predict what a reaction’s major products will be.

The researchers’ work appears in the American Chemical Society’s journal Central Science. Like all machine-learning systems, theirs presents its results in terms of probabilities. In tests, the system was able to predict a reaction’s major product 72 percent of the time; 87 percent of the time, it ranked the major product among its three most likely results.

“There’s clearly a lot understood about reactions today,” says Klavs Jensen, the Warren K. Lewis Professor of Chemical Engineering at MIT and one of four senior authors on the paper, “but it’s a highly evolved, acquired skill to look at a molecule and decide how you’re going to synthesize it from starting materials.”

With the new work, Jensen says, “the vision is that you’ll be able to walk up to a system and say, ‘I want to make this molecule.’ The software will tell you the route you should make it from, and the machine will make it.”

With a 72 percent chance of identifying a reaction’s chief product, the system is not yet ready to anchor the type of completely automated chemical synthesis that Jensen envisions. But it could help chemical engineers more quickly converge on the best sequence of reactions — and possibly suggest sequences that they might not otherwise have investigated.

Jensen is joined on the paper by first author Connor Coley, a graduate student in chemical engineering; William Green, the Hoyt C. Hottel Professor of Chemical Engineering, who, with Jensen, co-advises Coley; Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science; and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science.

Acting locally

A single organic molecule can consist of dozens and even hundreds of atoms. But a reaction between two such molecules might involve only two or three atoms, which break their existing chemical bonds and form new ones. Thousands of reactions between hundreds of different reagents will often boil down to a single, shared reaction between the same pair of “reaction sites.”

Provides readers with detailed summaries of online discussions

From Reddit to Quora, discussion forums can be equal parts informative and daunting. We’ve all fallen down rabbit holes of lengthy threads that are impossible to sift through. Comments can be redundant, off-topic or even inaccurate, but all that content is ultimately still there for us to try and untangle.

Sick of the clutter, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed “Wikum,” a system that helps users construct concise, expandable summaries that make it easier to navigate unruly discussions.

“Right now, every forum member has to go through the same mental labor of squeezing out key points from long threads,” says MIT Professor David Karger, who was senior author on a new paper about Wikum. “If every reader could contribute that mental labor back into the discussion, it would save that time and energy for every future reader, making the conversation more useful for everyone.”

The team tested Wikum against a Google document with tracked changes that aimed to mimic the collaborative editing structure of a wiki. They found that Wikum users completed reading much faster and recalled discussion points more accurately, and that editors made edits 40 percent faster.

Karger wrote the new paper with PhD students Lea Verou and Amy Zhang, who was lead author. The team presented the work last week at ACM’s Conference on Computer-Supported Cooperative Work and Social Computing in Portland, Oregon.

How it works

While wikis can be a good way for people to summarize discussions, they aren’t ideal because users can’t see what’s already been summarized. This makes it difficult to break summarizing down into small steps that can be completed by individual users, because it requires that they spend a lot of energy figuring out what needs to happen next. Meanwhile, forums like Reddit let users “upvote” the best answers or comments, but lack contextual summaries that help readers get detailed overviews of discussions.

Wikum bridges the gap between forums and wikis by letting users work in small doses to refine a discussion’s main points, and giving readers an overall “map” of the conversation.

The extremely high resolution of 3 D printers

Today’s 3-D printers have a resolution of 600 dots per inch, which means that they could pack a billion tiny cubes of different materials into a volume that measures just 1.67 cubic inches.

Such precise control of printed objects’ microstructure gives designers commensurate control of the objects’ physical properties — such as their density or strength, or the way they deform when subjected to stresses. But evaluating the physical effects of every possible combination of even just two materials, for an object consisting of tens of billions of cubes, would be prohibitively time consuming.

So researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a new design system that catalogues the physical properties of a huge number of tiny cube clusters. These clusters can then serve as building blocks for larger printable objects. The system thus takes advantage of physical measurements at the microscopic scale, while enabling computationally efficient evaluation of macroscopic designs.

“Conventionally, people design 3-D prints manually,” says Bo Zhu, a postdoc at CSAIL and first author on the paper. “But when you want to have some higher-level goal — for example, you want to design a chair with maximum stiffness or design some functional soft [robotic] gripper — then intuition or experience is maybe not enough. Topology optimization, which is the focus of our paper, incorporates the physics and simulation in the design loop. The problem for current topology optimization is that there is a gap between the hardware capabilities and the software. Our algorithm fills that gap.”

Zhu and his MIT colleagues presented their work this week at Siggraph, the premier graphics conference. Joining Zhu on the paper are Wojciech Matusik, an associate professor of electrical engineering and computer science; Mélina Skouras, a postdoc in Matusik’s group; and Desai Chen, a graduate student in electrical engineering and computer science.

Points in space

The MIT researchers begin by defining a space of physical properties, in which any given microstructure will assume a particular location. For instance, there are three standard measures of a material’s stiffness: One describes its deformation in the direction of an applied force, or how far it can be compressed or stretched; one describes its deformation in directions perpendicular to an applied force, or how much its sides bulge when it’s squeezed or contract when it’s stretched; and the third measures its response to shear, or a force that causes different layers of the material to shift relative to each other.

Those three measures define a three-dimensional space, and any particular combination of them defines a point in that space.

Focuses on marine microbes and microbial communities

Microbes mediate the global marine cycles of elements, modulating atmospheric carbon dioxide and helping to maintain the oxygen we all breathe, yet there is much about them scientists still don’t understand. Now, an award from the Simons Foundation will give researchers from MIT’s Darwin Project access to bigger, better computing resources to model these communities and probe how they work.

The simulations of plankton populations made by Darwin Project researchers have become increasingly computationally demanding. MIT Professor Michael “Mick” Follows and Principal Research Engineer Christopher Hill, both affiliates of the Darwin Project, were therefore delighted to learn of their recent Simons Foundation award, providing them with enhanced compute infrastructure to help execute the simulations of ocean circulation, biogeochemical cycles, and microbial population dynamics that are the bread and butter of their research.

The Darwin Project, an alliance between oceanographers and microbiologists in the MIT Department of Earth, Atmospheric and Planetary Sciences (EAPS) and the Parsons Lab in the MIT Department of Civil and Environmental Engineering, was conceived as an initiative to “advance the development and application of novel models of marine microbes and microbial communities, identifying the relationships of individuals and communities to their environment, connecting cellular-scale processes to global microbial community structure” with the goal of coupling “state of the art physical models of global ocean circulation with biogeochemistry and genome-informed models of microbial processes.”

In response to increases in model complexity and resolution over the course of past decade since the project’s inception in 2007, computational demands have ballooned. Increased fidelity and algorithmic sophistication in both biological and fluid dynamical component models and forays into new statistical analysis approaches, leveraging big-data innovations to analyze the simulations and field data, have grown inexorably.