Aeronautical and astronautical engineering

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MIT-IBM Watson AI Lab seed to signal: Amplifying early-career faculty impact

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Careers, Collaboration, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Industry, Laboratory for Information and Decision Systems (LIDS), Machine Learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Natural Language Processing, Research, Robotics, School of Engineering

The early years of faculty members’ careers are a formative and exciting time in which to establish a firm footing that helps determine the trajectory of researchers’ studies. This includes building a research team, which demands innovative ideas and direction, creative collaborators, and reliable resources. For a group of MIT faculty working with and on artificial […]

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A better method for planning complex visual tasks

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer science and technology, Computer vision, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, Robotics, School of Engineering

MIT researchers have developed a generative artificial intelligence-driven approach for planning long-term visual tasks, like robot navigation, that is about twice as effective as some existing techniques.Their method uses a specialized vision-language model to perceive the scenario in an image and simulate actions needed to reach a goal. Then a second model translates those simulations

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MIT engineers design an aerial microrobot that can fly as fast as a bumblebee

Aeronautical and astronautical engineering, Artificial Intelligence, Bioinspiration, Drones, Electrical engineering and computer science (EECS), Electronics, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, Research Laboratory of Electronics, Robotics, School of Engineering

In the future, tiny flying robots could be deployed to aid in the search for survivors trapped beneath the rubble after a devastating earthquake. Like real insects, these robots could flit through tight spaces larger robots can’t reach, while simultaneously dodging stationary obstacles and pieces of falling rubble.So far, aerial microrobots have only been able

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Teaching robots to map large environments

Aeronautical and astronautical engineering, Algorithms, Artificial Intelligence, Autonomous vehicles, Computer science and technology, Computer vision, Disaster response, Electrical engineering and computer science (EECS), Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, Robotics, School of Engineering

A robot searching for workers trapped in a partially collapsed mine shaft must rapidly generate a map of the scene and identify its location within that scene as it navigates the treacherous terrain.Researchers have recently started building powerful machine-learning models to perform this complex task using only images from the robot’s onboard cameras, but even

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New prediction model could improve the reliability of fusion power plants

Aeronautical and astronautical engineering, Artificial Intelligence, Computer modeling, Energy, Fluid dynamics, Fusion, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Nuclear power and reactors, Plasma Science and Fusion Center, Renewable energy, Research, School of Engineering, Sustainability

Tokamaks are machines that are meant to hold and harness the power of the sun. These fusion machines use powerful magnets to contain a plasma hotter than the sun’s core and push the plasma’s atoms to fuse and release energy. If tokamaks can operate safely and efficiently, the machines could one day provide clean and

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AI and machine learning for engineering design

Aeronautical and astronautical engineering, Artificial Intelligence, Civil and environmental engineering, Classes and programs, Computer modeling, Design, Faculty, Invention, Machine Learning, Mechanical engineering, MIT Sloan School of Management, Nuclear science and engineering, School of Engineering, Students

Artificial intelligence optimization offers a host of benefits for mechanical engineers, including faster and more accurate designs and simulations, improved efficiency, reduced development costs through process automation, and enhanced predictive maintenance and quality control.“When people think about mechanical engineering, they’re thinking about basic mechanical tools like hammers and … hardware like cars, robots, cranes, but mechanical engineering

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DOE selects MIT to establish a Center for the Exascale Simulation of Coupled High-Enthalpy Fluid–Solid Interactions

Aeronautical and astronautical engineering, Artificial Intelligence, Computer modeling, Computer science and technology, Department of Energy (DoE), DMSE, Electrical engineering and computer science (EECS), Machine Learning, Mathematics, Mechanical engineering, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, Security studies and military, Space, Supercomputing

The U.S. Department of Energy’s National Nuclear Security Administration (DOE/NNSA) recently announced that it has selected MIT to establish a new research center dedicated to advancing the predictive simulation of extreme environments, such as those encountered in hypersonic flight and atmospheric re-entry. The center will be part of the fourth phase of NNSA’s Predictive Science Academic Alliance

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