The idea of a floating city may sound like a concept from the latest science fiction blockbuster, but it's set to become a reality in Busan, South Korea, where scientists backed by the UN are building the world's first prototype floating city. The project, called OCEANIX, was announced last year, but new design images have now been unveiled. They show how interconnected platforms will cover a total of 15.5 acres, with enough space to accommodate 12,000 people. Construction of the floating city is estimated to cost $200 million (£150 million), and is due to be completed by 2025. 'We are on track to delivering OCEANIX Busan and demonstrating that floating infrastructure can create new land for coastal cities looking for sustainable ways to expand onto the ocean, while adapting to sea level rise,' said Philipp Hofmann, CEO of OCEANIX.

Workshop hosted by MIT's Climate and Sustainability Consortium, MIT-IBM Watson AI Lab, and the MIT Schwarzman College of Computing highlights how new approaches to computing can save energy and help the planet. The voracious appetite for energy from the world's computers and communications technology presents a clear threat for the globe's warming climate. That was the blunt assessment from presenters in the intensive two-day Climate Implications of Computing and Communications workshop held on March 3 and 4, hosted by MIT's Climate and Sustainability Consortium (MCSC), MIT-IBM Watson AI Lab, and the Schwarzman College of Computing. The virtual event featured rich discussions and highlighted opportunities for collaboration among an interdisciplinary group of MIT faculty and researchers and industry leaders across multiple sectors -- underscoring the power of academia and industry coming together. "If we continue with the existing trajectory of compute energy, by 2040, we are supposed to hit the world's energy production capacity. The increase in compute energy and demand has been increasing at a much faster rate than the world energy production capacity increase," said Bilge Yildiz, the Breene M. Kerr Professor in the MIT departments of Nuclear Science and Engineering and Materials Science and Engineering, one of the workshop's 18 presenters.

Identification of unknown physical processes and parameters of groundwater contaminant sources is a challenging task due to their ill-posed and non-unique nature. Numerous works have focused on determining nonlinear physical processes through model selection methods. However, identifying corresponding nonlinear systems for different physical phenomena using numerical methods can be computationally prohibitive. With the advent of machine learning (ML) algorithms, more efficient surrogate models based on neural networks (NNs) have been developed in various disciplines. In this work, a theory-guided U-net (TgU-net) framework is proposed for surrogate modeling of three-dimensional (3D) groundwater contaminant problems in order to efficiently elucidate their involved processes and unknown parameters. In TgU-net, the underlying governing equations are embedded into the loss function of U-net as soft constraints. For the considered groundwater contaminant problem, sorption is considered to be a potential process of an uncertain type, and three equilibrium sorption isotherm types (i.e., linear, Freundlich, and Langmuir) are considered. Different from traditional approaches in which one model corresponds to one equation, these three sorption types are modeled through only one TgU-net surrogate. The three mentioned sorption terms are integrated into one equation by assigning indicators. Accurate predictions illustrate the satisfactory generalizability and extrapolability of the constructed TgU-net. Furthermore, based on the constructed TgU-net surrogate, a data assimilation method is employed to identify the physical process and parameters simultaneously. This work shows the possibility of governing equation discovery of physical problems that contain multiple and even uncertain processes by using deep learning and data assimilation methods.

As the world increasingly turns to renewable energy sources to power our homes and businesses, the need for high-performance computing (HPC) and artificial intelligence (AI) is also increasing. HPC and AI are used to model and predict complex phenomena, like weather patterns and climate change, as well as to optimize the design of renewable energy systems. The demand for HPC and AI is therefore increasing in many industries that are critical to the transition to a low-carbon economy. In addition, a great deal of research and development (R&D) has been put into play using these technologies, which are leading to breakthroughs that promise to change the way people live and work. With supercomputing technology in the limelight and companies focusing on enhancing their data centers' performance, it's easy to get caught up in the hype surrounding the new computer systems that boast high computing power. But a lot of people aren't sure where all of this is going or why it's such a big deal.

The voracious appetite for energy from the world's computers and communications technology presents a clear threat for the globe's warming climate. That was the blunt assessment from presenters in the intensive two-day Climate Implications of Computing and Communications workshop held on March 3 and 4, hosted by MIT's Climate and Sustainability Consortium (MCSC), MIT-IBM Watson AI Lab, and the Schwarzman College of Computing. The virtual event featured rich discussions and highlighted opportunities for collaboration among an interdisciplinary group of MIT faculty and researchers and industry leaders across multiple sectors -- underscoring the power of academia and industry coming together. "If we continue with the existing trajectory of compute energy, by 2040, we are supposed to hit the world's energy production capacity. The increase in compute energy and demand has been increasing at a much faster rate than the world energy production capacity increase," said Bilge Yildiz, the Breene M. Kerr Professor in the MIT departments of Nuclear Science and Engineering and Materials Science and Engineering, one of the workshop's 18 presenters.

MIT research scientists Pablo Rodriguez-Fernandez and Nathan Howard have just completed one of the most demanding calculations in fusion science -- predicting the temperature and density profiles of a magnetically confined plasma via first-principles simulation of plasma turbulence. Solving this problem by brute force is beyond the capabilities of even the most advanced supercomputers. Instead, the researchers used an optimization methodology developed for machine learning to dramatically reduce the CPU time required while maintaining the accuracy of the solution. Fusion offers the promise of unlimited, carbon-free energy through the same physical process that powers the sun and the stars. It requires heating the fuel to temperatures above 100 million degrees, well above the point where the electrons are stripped from their atoms, creating a form of matter called plasma.

When you think of robotic surgery, you might think of remotely controlled robotic arms whirring over a patient, or tiny endoscopic cameras that help surgeons navigate with precise instruments. You probably don't think of a magnetically controlled slime robot slithering through your gastrointestinal tract and swallowing objects, like some kind of sci-fi ooze. But that's the exact idea behind the Reconfigurable Magnetic Slime Robot -- a stretchy, sluglike robot that can squeeze through tight spaces, wrap around objects and even "self heal" after it's been cut in two. Researcher Li Zhang says the Reconfigurable Magnetic Slime Robot is soft and stretchy enough to go inside the human body and swallow foreign objects. Created by a team of researchers at the Chinese University of Hong Kong, the Slime Robot is a non-Newtonian fluid, meaning it can behave both as a solid and a liquid.

MIT research scientists Pablo Rodriguez-Fernandez and Nathan Howard have just completed one of the most demanding calculations in fusion science -- predicting the temperature and density profiles of a magnetically confined plasma via first-principles simulation of plasma turbulence. Solving this problem by brute force is beyond the capabilities of even the most advanced supercomputers. Instead, the researchers used an optimization methodology developed for machine learning to dramatically reduce the CPU time required while maintaining the accuracy of the solution. Fusion energy Fusion offers the promise of unlimited, carbon-free energy through the same physical process that powers the sun and the stars. It requires heating the fuel to temperatures above 100 million degrees, well above the point where the electrons are stripped from their atoms, creating a form of matter called plasma.

Facebook's parent company, Meta, has used AI to develop a new way of creating concrete which it claims produces 40 per cent less carbon emissions than standard mixtures, and is already using it in its latest data centre. But experts say that concrete mixtures with similar emissions are already in use across Europe, and that constructing new buildings is incompatible with reducing carbon pollution. Meta is investing heavily in AI research, including building the world's most powerful AI-specific supercomputer. Its main aims are to develop better speech-recognition tools, automatically translate between different languages and help build a 3D virtual metaverse, but the company is also using AI to work on projects such as concrete production. The company says that this construction material is a major contributor to its carbon footprint as it builds data centres around the world for its online services.

Supercomputers help researchers study the causes and effects--usually in that order--of complex phenomena. However, scientists occasionally need to deduce the origins of scientific phenomena based on observable results. These so-called inverse problems are notoriously difficult to solve, especially when the amount of data that must be analyzed outgrows traditional machine-learning tools. To better understand inverse problems, a team from the US Department of Energy's (DOE's) Oak Ridge National Laboratory (ORNL), NVIDIA, and Uber Technologies developed and demonstrated two new techniques within a widely used communication library called Horovod. Developed by Uber, this platform trains deep neural networks (DNNs) that use algorithms to imitate and harness the decision-making power of the human brain for scientific applications. Because Horovod relies on a single coordinator to provide instructions to many different workers (i.e., GPUs in this case) to complete this process, large-scale deep-learning applications often encounter significant slowdowns during training.