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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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How to build AI scaling laws for efficient LLM training and budget maximization

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering

When researchers are building large language models (LLMs), they aim to maximize performance under a particular computational and financial budget. Since training a model can amount to millions of dollars, developers need to be judicious with cost-impacting decisions about, for instance, the model architecture, optimizers, and training datasets before committing to a model. To anticipate

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What does the future hold for generative AI?

Artificial Intelligence, Collaboration, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Industry, Machine Learning, MIT Schwarzman College of Computing, MIT Sloan School of Management, President Sally Kornbluth, Provost, Research, Robotics, School of Architecture and Planning, School of Engineering, School of Humanities Arts and Social Sciences, School of Science, Special events and guest speakers, Startups, Technology and society

When OpenAI introduced ChatGPT to the world in 2022, it brought generative artificial intelligence into the mainstream and started a snowball effect that led to its rapid integration into industry, scientific research, health care, and the everyday lives of people who use the technology.What comes next for this powerful but imperfect tool?With that question in

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New tool makes generative AI models more likely to create breakthrough materials

Artificial Intelligence, Department of Energy (DoE), Energy, Machine Learning, Materials science and engineering, National Science Foundation (NSF), Nuclear science and engineering, Quantum physics, Research, School of Engineering, Superconductivity

The artificial intelligence models that turn text into images are also useful for generating new materials. Over the last few years, generative materials models from companies like Google, Microsoft, and Meta have drawn on their training data to help researchers design tens of millions of new materials.But when it comes to designing materials with exotic

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New AI system could accelerate clinical research

Artificial Intelligence, Broad Institute, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Health, Health care, Imaging, Machine Learning, Medicine, MIT Schwarzman College of Computing, National Institutes of Health (NIH), Research, School of Engineering

Annotating regions of interest in medical images, a process known as segmentation, is often one of the first steps clinical researchers take when running a new study involving biomedical images.For instance, to determine how the size of the brain’s hippocampus changes as patients age, the scientist first outlines each hippocampus in a series of brain

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AI system learns from many types of scientific information and runs experiments to discover new materials

Artificial Intelligence, DMSE, Energy, Machine Learning, Materials science and engineering, MIT.nano, Nanoscience and nanotechnology, Nuclear science and engineering, Research, School of Engineering

Machine-learning models can speed up the discovery of new materials by making predictions and suggesting experiments. But most models today only consider a few specific types of data or variables. Compare that with human scientists, who work in a collaborative environment and consider experimental results, the broader scientific literature, imaging and structural analysis, personal experience

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Responding to the climate impact of generative AI

Algorithms, Artificial Intelligence, Climate change, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Emissions, Energy, Energy storage, infrastructure, Lincoln Laboratory, Machine Learning, MIT Energy Initiative, MIT Schwarzman College of Computing, MIT Sloan School of Management, Research, School of Engineering, Supercomputing, Sustainable computing

In part 2 of our two-part series on generative artificial intelligence’s environmental impacts, MIT News explores some of the ways experts are working to reduce the technology’s carbon footprint.The energy demands of generative AI are expected to continue increasing dramatically over the next decade.For instance, an April 2025 report from the International Energy Agency predicts that the global

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3 Questions: The pros and cons of synthetic data in AI

Algorithms, Artificial Intelligence, Computer science and technology, data, Electrical engineering and computer science (EECS), Interview, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering

Synthetic data are artificially generated by algorithms to mimic the statistical properties of actual data, without containing any information from real-world sources. While concrete numbers are hard to pin down, some estimates suggest that more than 60 percent of data used for AI applications in 2024 was synthetic, and this figure is expected to grow

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A greener way to 3D print stronger stuff

3-D printing, Artificial Intelligence, Cleaner industry, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Design, Electrical engineering and computer science (EECS), Invention, Machine Learning, MIT Schwarzman College of Computing, Recycling, Research, School of Engineering, Stock Market, Sustainability

3D printing has come a long way since its invention in 1983 by Chuck Hull, who pioneered stereolithography, a technique that solidifies liquid resin into solid objects using ultraviolet lasers. Over the decades, 3D printers have evolved from experimental curiosities into tools capable of producing everything from custom prosthetics to complex food designs, architectural models,

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