From adaptive problem sequencing to physics-informed simulations, AI is transforming how students learn and apply physics. New tools now guide learners step-by-step, visualize complex concepts, and ...

Scientists are blending physics-informed AI with supercomputing to model plasma turbulence more accurately and efficiently. These breakthroughs could improve predictions for fusion reactors, ...

Nebius Group NV, a Dutch operator of artificial intelligence data centers, today announced plans to buy software maker Eigen ...

Abstract: Neural operators have emerged as a powerful tool for learning mappings between function spaces, particularly for solving partial differential equations (PDEs). This study introduces a novel ...

QuanONet is a pure quantum neural operator framework designed for the Noisy Intermediate-Scale Quantum (NISQ) era to solve partial differential equations (PDEs). . ├── main.py # Unified entry point ...

One of the long-term goals of artificial intelligence (AI) is to build machines that can continually learn new knowledge from their experiences, ground these experiences in the physical world, and ...

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 ...

(a) Unified Model: NO works across various undersampling patterns, unlike CNNs (e.g., E2E-VarNet) that need separate models for each. (b) Consistent Performance: NO consistently outperforms CNNs, ...