In recent years, the frequency of weather-related natural disasters—cyclones, torrential rains, floods—has increased as a consequence of global warming. These disasters cause billions of dollars in ...

Black-box optimization, particularly Bayesian optimization, is a practical approach for weather-intervention design, achieving meaningful rainfall ...

Explore predictive modeling for compound prioritization, including in silico screening, toxicology models, and lead selection ...

Accurate sunlight data is becoming essential for the clean-energy transition, but tracking how much solar radiation reaches ...

Abstract: Accurate estimation of remaining useful life (RUL) in aeroengines is essential for improving safety, reducing operational costs, and optimizing maintenance strategies within the aviation ...

Abstract: The conventional geo-electromagnetic data inversions are mostly based on gradient optimization methods. However, this type of method can only provide a single “optimal” inverse model under ...

Neither Sakana AI nor its external AI service providers will use customer data or inputs for model training or fine-tuning unless the client provides explicit opt-in consent.

A deep reinforcement learning framework optimizes silicon-based photonic crystal fiber modulators, achieving ultra-low ...

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THE American Thoracic Society (ATS) International Conference 2026, held in Orlando, Florida, USA, May 15–20, united the pulmonary medicine community for a week of discussion on the latest data, ...

Brochu, E., Cora, V.M. and de Freitas, N. (2010) A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning.

In 2022, global production of construction materials accounted for more than 7% of total carbon emissions. But how many of ...