Spatially distributed prediction of streamflow and nitrogen (N) export dynamics is essential for precision management of ...
The seismic crisis that gripped the Greek island of Santorini and its neighbors in 2025 contained more than 60,000 ...
The bipedal wheel-legged robot combines the high energy efficiency of wheeled movement with the terrain adaptability of legged locomotion. However, achieving a smooth transition between these two ...
Gaussian Process-Based Learning Model Predictive Control With Application to Flywheel Battery System
Abstract: The flywheel battery system is extremely sensitive to its own time-varying nonlinear characteristics and random disturbances in actual operating conditions. The traditional model predictive ...
Researchers at Shanghai University have developed a physics-constrained, data-efficient artificial intelligence framework that enables accurate thermal field inversion in chiplet-based packaging ...
Abstract: Robot interaction control with variable impedance parameters may conform to task requirements during continuous interaction with dynamic environments. Iterative learning (IL) is effective to ...
This important work introduces a family of interpretable Gaussian process models that allows us to learn and model sequence-function relationships in biomolecules. These models are applied to three ...
Spatiotemporal Gaussian process modeling for environmental data: non-stationary PDE prior, deep kernels, multi-fidelity fusion, and A-optimal sampling.非稳态 PDE ...
Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood ...
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