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 ...
(A–C) Representative images reconstructed by conventional method (left) and new method (right) of microtubules, nuclear pore complexes and F-actin samples. The regions enclosed by the white boxes are ...
Researchers present a comprehensive review of frontier AI applications in computational structural analysis from 2020 to 2025, focusing on graph neural networks (GNNs), sequence-to-sequence (Seq2Seq) ...
One of the key steps in developing new materials is property identification, which has long relied on massive amounts of experimental data and expensive equipment, limiting research efficiency. A ...
We solve the one-dimensional Fokker-Planck equation for radiation belt electrons under the assumption of the conservation of the first and second adiabatic invariants (so-called radial diffusion). We ...
ANKARA, TURKIYE - OCTOBER 8: An infographic titled "2024 Nobel Prize" created in Ankara, Turkiye on October 8, 2024. 2024 Nobel Prize in physics awarded to John J. Hopfield, Geoffrey E. Hinton for ...
The 2024 Nobel Prize in Physics has been awarded to scientists John Hopfield and Geoffrey Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural ...
Deep-Learning Paradigm Achieves Global Precision in Nuclear Charge Density PredictionsThe charge density distribution of an atomic nucleus is a ...
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