When engineers at Sumitomo Riko needed to speed up the design cycle for automotive rubber and polymer components, they turned ...

David J. Silvester, a mathematics professor at the University of Manchester, has developed a novel machine-learning method to ...

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 ...

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 ...

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) ...

Genie Sim 3.0 integrates 3D reconstruction, visual generation, and physics engines to enable faster sim-to-real transfer.

Researchers at The University of Manchester have created a physics‑informed machine‑learning model that can run molecular ...

Demis Hassabis (DeepMind CEO) and other AI leaders sees the next big AI gains—and the path to AGI—will come from targeted ...

During surgery to correct an abnormal heartbeat, doctors rely on a mix of imaging and inference. Still, many critical details remain hidden. At RIT, artificial intelligence (AI) researchers want to ...

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 ...