Differential equations are fundamental tools in physics: they are used to describe phenomena ranging from fluid dynamics to general relativity. But when these equations become stiff (i.e. they involve ...
Physics-informed neural networks (PINNs) represent a burgeoning paradigm in computational science, whereby deep learning frameworks are augmented with explicit physical laws to solve both forward and ...
Researchers from the Institute of Cosmos Sciences of the University of Barcelona (ICCUB) have developed a new framework based ...
Researchers at Seoul National University and Kyung Hee University report a framework to control collective motions, such as ...
The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...
As artificial intelligence explodes in popularity, two of its pioneers have nabbed the 2024 Nobel Prize in physics. The prize surprised many, as these developments are typically associated with ...
John Hopfield and Geoffrey Hinton won the Nobel Prize in Physics for their work on artificial neural networks and machine learning. Jonathan Nackstrand / AFP via Getty Images A pair of scientists—John ...
Optical coherence tomography (OCT) is an imaging technology that can non-invasively generate cross-sectional images of tissue. OCT is widely used in eye clinics to diagnose and manage retinal diseases ...
With work on machine learning that uses artificial neural networks, John J. Hopfield and Geoffrey E. Hinton “showed a completely new way for us to use computers,” the committee said. By Derrick Bryson ...
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