Physicists finally know how much faster time moves on the Red Planet. Breakthroughs, discoveries, and DIY tips sent every weekday. Tracking the first astronauts' visit to Mars won't be as simple as watching a clock or marking days off of a calendar. Thanks to relativity, time actually moves faster on the Red Planet than it does here on Earth. For years, scientists have wondered about the exact temporal difference between planets, but physicists at the National Institute of Standards and Technology (NIST) finally have an answer.

First formulated by Sir Isaac Newton in his work "Philosophiae Naturalis Principia Mathematica", the concept of the Three-Body Problem was put forth as a study of the motion of the three celestial bodies within the Earth-Sun-Moon system. In a generalized definition, it seeks to predict the motion for an isolated system composed of three point masses freely interacting under Newton's law of universal attraction. This proves to be analogous to a multitude of interactions between celestial bodies, and thus, the problem finds applicability within the studies of celestial mechanics. Despite numerous attempts by renowned physicists to solve it throughout the last three centuries, no general closed-form solutions have been reached due to its inherently chaotic nature for most initial conditions. Current state-of-the-art solutions are based on two approaches, either numerical high-precision integration or machine learning-based. Notwithstanding the breakthroughs of neural networks, these present a significant limitation, which is their ignorance of any prior knowledge of the chaotic systems presented. Thus, in this work, we propose a novel method that utilizes Physics-Informed Neural Networks (PINNs). These deep neural networks are able to incorporate any prior system knowledge expressible as an Ordinary Differential Equation (ODE) into their learning processes as a regularizing agent. Our findings showcase that PINNs surpass current state-of-the-art machine learning methods with comparable prediction quality. Despite a better prediction quality, the usability of numerical integrators suffers due to their prohibitively high computational cost. These findings confirm that PINNs are both effective and time-efficient open-form solvers of the Three-Body Problem that capitalize on the extensive knowledge we hold of classical mechanics.

Scientists keep taking their own lives, and no one knows why. That's the central mystery at the start of 3 Body Problem, the new Netflix series based on a trilogy of sci-fi novels by Chinese author Cixin Liu. But it soon unfolds into something far grander: There's a mysterious VR video game, flashbacks to revolutionary China, shady billionaires, and strange cults. Liu's novels are beloved in China and have a smaller but similarly dedicated following among English-language readers, but they are hard science fiction--heavy on concept, light on character. More than once in the series, someone resorts to wheeling out a chalkboard to make their point, and there are scenes in the books that seem impossible to film: multidimensional structures collapsing in on themselves, a computer made up of millions of soldiers, nano-wires cutting through steel, diamond, flesh.

Sign up to receive our weekly cultural-recommendations newsletter. Science fiction has historically been considered a niche genre, one in which far-flung scenarios play out on distant planets. Today, though, such plots are at the center of our media landscape. The hosts are joined by Joshua Rothman, an editor and writer at The New Yorker, who makes the case for science fiction as an extension of the realist novel, tracing the way films like "The Matrix" and "Contagion" have shed new light on modern life. The boundaries between science fiction and reality are increasingly blurred: tech founders like Elon Musk and Jeff Bezos have cited classic sci-fi texts as inspiration, and terms like "red-pilling" have found their way into our political vernacular.

This work utilizes a particle swarm optimizer (PSO) for initial orbit determination for a chief and deputy scenario in the circular restricted three-body problem (CR3BP). The PSO is used to minimize the difference between actual and estimated observations and knowledge of the chief's position with known CR3BP dynamics to determine the deputy's initial state. Convergence is achieved through limiting particle starting positions to feasible positions based on the known chief position, and sensor constraints. Parallel and GPU processing methods are used to improve computation time and provide an accurate initial state estimate for a variety of cislunar orbit geometries.

Can AI teach itself the laws of physics? Will classical computers soon be replaced by deep neural networks? Sure looks like it, if you've been following the news, which lately has been filled with headlines like, "A neural net solves the three-body problem 100 million times faster: Machine learning provides an entirely new way to tackle one of the classic problems of applied mathematics," and "Who needs Copernicus if you have machine learning?". The latter was described by another journalist, in an article called "AI Teaches Itself Laws of Physics," as a "monumental moment in both AI and physics," which "could be critical in solving quantum mechanics problems." The trouble is, the authors have given no compelling reason to think that they could actually do this.

The mind-bending calculations required to predict how three heavenly bodies orbit each other have baffled physicists since the time of Sir Isaac Newton. Now artificial intelligence (A.I.) has shown that it can solve the problem in a fraction of the time required by previous approaches. Newton was the first to formulate the problem in the 17th century, but finding a simple way to solve it has proved incredibly difficult. The gravitational interactions between three celestial objects like planets, stars and moons result in a chaotic system -- one that is complex and highly sensitive to the starting positions of each body. Current approaches to solving these problems involve using software that can take weeks or even months to complete calculations.

Science Fiction writing in itself is an exercise of modeling the future based on present and past culture. Therefore, integrating predictive algorithms in literature works has been an intuitive choice. Isaac Asimov was a prolific 20th century American writer and professor. He wrote or edited more than 500 books and is one of the most popular SF writers of all time. The Foundation Series is one of his most notable and comprehensive works.

My first thought after reading that sentence was: "Why didn't Amazon recommend me that book when I was buying Camille's?" Then I started reading the Three-Body Problem by Cixin Liu. In the first part of that book they mention another book called Silent Spring, which according to Three-Body, it seems to have been censored by the Cultural Revolution. Without spoiling the story, Silent Spring is a very important element inside the story, up to the point that later I realised the first part of the book is actually called Silent Spring. Inside Three-Body, the book Silent Spring is only mentioned by the characters here and there, and we realise it's important to know about it once we have gone through half the book. While it's not essential to have read Silent Spring in order to understand Three-Body, it seems like quite an interesting book to have. So while reading this I also wondered: "Why didn't Amazon recommend me that book when I was buying the Three-Body Problem?" Finally I've just started reading Nobel Prize winner Svetlana Alexievich's The Unwomanly Face of War, which is an account of the Soviet women that fought during WWII.

Two years ago, a Chinese chip-design expert named Micree Zhan was reading China's seminal science-fiction novel, The Three-Body Problem, by Liu Cixin, while wrestling with how to create a new processor. He had already designed custom chips for the company he co-founded, Bitmain, that had made it into the world's leading bitcoin miner, allowing it to dominate the new, hyper-competitive industry of unearthing bitcoins. Now he needed a chip that could launch Bitmain onto a new trajectory, one that would help it master a world-altering technology called deep learning, a branch of artificial intelligence. While performing his nightly meditation, a practice he has kept up for nearly a decade, it suddenly came to Zhan. "It was late at night, and something inspired me--Sophon!" he recalls. A sophon is a fictional proton-sized supercomputer from The Three-Body Problem that is sent by an alien civilization to halt scientific progress on Earth. The aliens use it to take over Earth when their own planet is destroyed by the chaotic gravitational forces of its three suns.