This digitally empowered approach typically uses general-purpose commercial finite element (FE) simulation tools such as ABAQUS (Dassault Systèmes, Vélizy-Villacoublay, France) or ANSYS (Canonsburg, ...

Using artificial intelligence to control digital manufacturing Researchers train a machine-learning model to monitor and adjust the 3D printing process to correct errors in real-time Date: August ...

Physics-based simulations, that staple of traditional HPC, may be evolving toward an emerging, AI-based technique that could radically accelerate simulation runs while cutting costs. Called “surrogate ...

Recovery of continuous 3D refractive index maps from discrete intensity-only measurements using neural fields. Nature Machine Intelligence, 2022; DOI: 10.1038/s42256-022-00530-3 ...

Project AirSim, like the original AirSim, uses AI models to build, train and test drones and other autonomous aircraft using 3D simulation.

A new technical paper titled “DeepOHeat: Operator Learning-based Ultra-fast Thermal Simulation in 3D-IC Design” was published (preprint) by researchers at UCSB and Cadence. Abstract “Thermal issue is ...

Jul 18, 2025 Advancing protein simulation with Machine Learning CGSchNet, a fast machine-learned model, simulates proteins with high accuracy, enabling drug discovery and protein engineering for ...

An international team led by Einstein Professor Cecilia Clementi in the Department of Physics at Freie Universität Berlin introduces a breakthrough in protein simulation.

More information: Sandro Wieser et al, Machine learned force-fields for an Ab-initio quality description of metal-organic frameworks, npj Computational Materials (2024). DOI: 10.1038/s41524-024 ...

There are also six new humanoid robot learning workflows for Project GR00T, to enable AI robot brain development, and new developer tools for video processing.