Artificial intelligence is revolutionizing physics by making complex concepts more intuitive, interactive, and personalized. From physics-informed neural networks to AI-powered simulations, these ...

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

Abstract: The reliability of multi-layer ceramic capacitors (MLCCs) under extreme conditions presents a critical challenge for modern electronics. Physics-based models, such as the Eyring framework ...

Researchers at The University of Manchester have created a physics‑informed machine‑learning model that can run molecular simulations for unprecedented lengths of time, even at temperatures as high as ...

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) ...

Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

ABSTRACT: Rubber is widely used in automotive vibration isolation systems due to its excellent mechanical properties and durability. However, elastomeric support components tend to experience ...

Accurate joint kinematics estimation is essential for understanding human movement and supporting biomechanical applications. Although optical motion capture systems are accurate, their high cost, ...

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c01600. Data set containing heterobifunctional degrader compound ...

One of the key steps in developing new materials is property identification, which has long relied on massive amounts of experimental data and expensive equipment, limiting research efficiency. A ...