Tag: physics-informed neural networks

A new editorial in Frontiers in Photonics highlights how physics-informed machine learning is transforming the design of metasurfaces, nonlinear metamaterials, ...

A new review shows that hybrid deep learning and swarm-optimized AI models are achieving near-perfect prediction of lithium-ion battery charge, ...

A hybrid graph neural network and deep unrolling architecture achieves 98 percent success in super-resolution direction-of-arrival estimation while remaining robust ...

Researchers combined machine learning with GAN and SMOGN data augmentation to accurately predict the tensile properties of sub-sized nuclear steel ...

Researchers used physics-informed neural networks to infer heterogeneous fault friction from GPS data of Japan's 2010 Bungo slow slip event, ...

A new critical review argues that artificial intelligence has accelerated epitope prediction for multi-epitope vaccines, but chemical developability, nanocarrier formulation ...

Researchers have built an AI framework that combines live video of floc formation, real-time turbidity sensing and physics-based equations to ...

A new physics-embedded machine learning framework called SPEL-VFL reconstructs hidden hydrogen energy flows in smart grids and pipeline networks even ...

Researchers have combined a laboratory wave tank, validated CFD simulations, a compact analytical formula, and a physics-informed neural network to ...

Researchers have developed a physics-informed neural network with an attention mechanism that predicts electric vehicle range with far greater accuracy ...