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

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 ...

Complex network theory has become a key analytical framework in modern physics for studying structure, dynamics, and emergent behaviour in complex systems.

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 ...

A breakthrough deterministic physics kernel delivers molecular, materials, and reaction screening across three ...

Less instrumentation. More insight. Physics-informed virtual sensors are shifting condition monitoring from isolated pilots to scalable, physics-based intelligence across assets. Here’s how SciML can ...

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.