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 ...

Ansys SimAI is a physics-agnostic and cloud-enabled computer-aided engineering tool that predicts performance of complex simulation scenarios. The game-changer? Engineers train their model and use it ...

Researchers at Harvard and Northwestern have developed a machine learning method that can design intrinsically disordered proteins with custom properties, addressing nearly 30% of all human proteins ...

Abstract: The AFOSR MURI effort, titled “A Robust Multi-Physics Design Analysis and Optimization Framework for Hypersonic Systems Grounded in Rigorous Model Reduction,” unites a multi-disciplinary ...

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

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.

AI meets isotope science: Machine learning is enhancing isotope analysis techniques, improving efficiency, accuracy, and insights into geochemical processes. Key hurdles remain: Data scarcity, limited ...