Understanding and predicting complex physical systems remain significant challenges in scientific research and engineering. Machine learning models, while powerful, often fail to follow the ...

A case study in aerospace manufacturing provides an overview of how physics-informed digital twin systems transform robotics processes—from adaptive process planning and real-time process monitoring ...

For decades, scientists have relied on structure to understand protein function. Tools like AlphaFold have revolutionized how researchers predict and design folded proteins, allowing for new ...

The convergence of advanced manufacturing, surface engineering, and digital technologies is transforming how materials and components are designed, ...

Recent research is advancing seismic hazard modeling through AI-driven soil liquefaction prediction, interpretable machine learning, physics-based simulations, and waveform-based probabilistic ...

Machine learning potential (MLP) training for surface reconstruction analyses. (a) Workflow for MLP training and large-scale configuration space searching. (b-c) Molecular dynamics (MD) simulations at ...

AI can be added to legacy motion control systems in three phases with minimal disruption: data collection via edge gateways, non-interfering anomaly detection and supervisory control integration.

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 ...