Researchers trained XGBoost machine learning models on automated finite element simulations to predict how special threaded connections respond to combined...

Researchers have developed DELTA, an unsupervised domain adaptation model that combines a grouped divergence attention module with self-paced deep clustering...

A new multimodal deep learning framework called GeoEPred combines protein language model semantics with predicted 3D structures to more accurately...

Researchers at UPES Dehradun have combined CFD simulations with machine learning, including a physics-guided neural network, to rapidly optimize drone...

Researchers have built a physics-informed, data-driven framework and a new screening index that link the composition of high-entropy alloys to...

A new LSTM-based dataset called MemoryFlux maps two decades of net ecosystem CO2 exchange across North America with unprecedented fidelity,...

Researchers at ETH Zürich have shown that self-supervised pretraining on simulated detector data can teach a single neural encoder to...

A neural network trained on ten years of all-sky images from Svalbard shows that clouds, not solar activity, govern when...

A new deep learning framework converts bearing vibration signals into dynamically weighted texture images and classifies them with a hybrid...

Researchers at Yanshan University have developed ADPOD, an outlier detection algorithm that improves density-peak clustering with an affinity-based density measure,...