Could AI Data Centers Be Moved to Outer Space? Massive data centers for generative AI are bad for the Earth. Data centers are being built at a frantic pace all over the world, driven by the AI boom. These facilities consume staggering amounts of electricity. By 2028, AI servers alone may use as much energy as 22 percent of US households.

Learning to simulate complex physics with graph networks.

You can never truly empty a box. Suppose you want to empty a box. You remove all its visible contents, pump out any gases, and--applying some science-fiction technology--evacuate any unseeable material such as dark matter. According to quantum mechanics, what's left inside? It sounds like a trick question.

Inverse problems describe the task of recovering an underlying signal of interest given observables. Typically, the observables are related via some non-linear forward model applied to the underlying unknown signal. Inverting the non-linear forward model can be computationally expensive, as it often involves computing and inverting a linearization at a series of estimates. Rather than inverting the physics-based model, we instead train a surrogate forward model (emulator) and leverage modern auto-grad libraries to solve for the input within a classical optimization framework. Current methods to train emulators are done in a black box supervised machine learning fashion and fail to take advantage of any existing knowledge of the forward model. In this article, we propose a simple learned weighted average model that embeds linearizations of the forward model around various reference points into the model itself, explicitly incorporating known physics. Grounding the learned model with physics based linearizations improves the forward modeling accuracy and provides richer physics based gradient information during the inversion process leading to more accurate signal recovery. We demonstrate the efficacy on an ocean acoustic tomography (OAT) example that aims to recover ocean sound speed profile (SSP) variations from acoustic observations (e.g.

Here are three smart tricks, based on an understanding of frictional forces, to beat a slippery slope. I don't know who invented this crazy challenge, but the idea is to put someone in a carved-out ice bowl and see if they can get out. The bowl is shaped like the inside of a sphere, so the higher up the sides you go, the steeper it gets. If you think an icy sidewalk is slippery, try going uphill on an icy sidewalk. What do you do when faced with a problem like this?