Results 151 to 160 of about 14,667 (177)

ParamNet: A Physics‐Guided Deep Learning Framework for Intelligent Self‐Inversion of Vacuum Optical Levitation Systems

Advanced Intelligent Systems, EarlyView.
A physics‐guided deep learning framework, ParamNet, is introduced for the intelligent self‐inversion of vacuum optical tweezers. By fuzing dual‐branch time–frequency features with physical dynamical constraints, it achieves high‐accuracy calibration of trap parameters from short‐window, low‐frequency trajectories, outperforming traditional methods ...
Qi Zheng, Wenfeng Fan, Feng Li, Jian Wu, Wei Quan   +4 more
wiley   +1 more source

Learning and inference with correlated neural variability. [PDF]

PNAS Nexus
Qi Y, Zhu Z, Wei Y, Cao L, Wang Z, Zhang J, Lu W, Feng J.   +7 more
europepmc   +1 more source

Memory efficient training for 3D brain image registration networks using PatchMorph. [PDF]

Sci Rep
Skibbe H, Byra M, Watakabe A, Yamamori T, Reisert M.   +4 more
europepmc   +1 more source

Reliability Assessment of Harmonic Reducers Based on the Two-Phase Hybrid Stochastic Degradation Process. [PDF]

Sensors (Basel)
Wei L, Liu P, Tian H, Li H, Qiu Y.
europepmc   +1 more source

Handling Multi-Source Uncertainty in Accelerated Degradation Through a Wiener-Based Robust Modeling Scheme. [PDF]

Sensors (Basel)
Tu H, Wang X, Li Y.
europepmc   +1 more source

Diffusion models for brain imaging computing: a survey of frameworks and applications. [PDF]

Brain Inform
Osman YBM, Elsanosi AHM, Jing C, Shen Y, Kuai H, Wang S, Ng MK, Lei B, Wang S.   +8 more
europepmc   +1 more source

Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes

Journal of Computational Physics, 2022
David J Warne, Thomas P Prescott, Ruth E Baker   +2 more
exaly  

Innovation Representation of Stochastic Processes With Application to Causal Inference

IEEE Transactions on Information Theory, 2020
Amichai Painsky, Saharon Rosset, Meir Feder   +2 more
exaly