Researchers from Skoltech and the Shanghai Institute of Optics and Fine Mechanics have developed an approach that helps ...
GOES-East satellite observations and machine learning have, for the first time, connected this observed structure to the much ...
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 ...
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 ...
ABSTRACT: This paper introduces a method to develop a common model based on machine learning (ML) that predicts the mechanical behavior of a family with three composite materials. The latter are ...
Spatiotemporal Gaussian process modeling for environmental data: non-stationary PDE prior, deep kernels, multi-fidelity fusion, and A-optimal sampling.非稳态 PDE ...
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