Simulating how atoms and molecules move over time is a central challenge in computational chemistry and materials science.
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Physics-trained AI models speed up engineering simulations and design work
Running a single physics simulation can take hours or days, depending on the complexity of the geometry and the equations ...
Hybrid climate modeling has emerged as an effective way to reduce the computational costs associated with cloud-resolving models while retaining their accuracy. The approach retains physics-based ...
Methodology of the CondensNet model. CondensNet is a physically constrained DL parametrisation coupled with a climate dynamics engine to support hybrid modelling. The network architecture mainly has ...
At SAE World Congress 2026, Luminary, the Physics AI company, today announced SHIFT-Crash, the first Physics AI model that predicts full-vehicle crash response, including deformation and stress fields ...
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) ...
The original version of this story appeared in Quanta Magazine. When she was 10 years old, Rose Yu got a birthday present that would change her life—and, potentially, the way we study physics. Her ...
Artificial intelligence (AI) is increasingly prevalent, integrated into phone apps, search engines and social media platforms as well as supporting myriad research applications. Of particular interest ...
Since the first FEA solver, Nastran, was developed for NASA in the 1960s, the simulation software industry has contended with a number of hurdles. For one, while the software (FEA, CFD, CEM) is ...
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