Erratum to: Knowledge‐driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un‐supervised learning (Magn Reson Med. 2024;92:496‐518)
Sign Up to like & getrecommendations! Published in 2024 at "Magnetic Resonance in Medicine"
DOI: 10.1002/mrm.30204
Abstract: Due to a misunderstanding of a vendor approach to their reconstruction algorithm read more here.
Keywords: deep learning; learning fast; reconstruction; driven deep ... See more keywords
Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Journal on Selected Areas in Communications"
DOI: 10.1109/jsac.2021.3087269
Abstract: This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for wideband millimeter-wave (mmWave) massive hybrid multiple-input multiple-output (MIMO) systems, where the angle-delay domain channels’ sparsity is exploited for reducing the overhead.… read more here.
Keywords: channel estimation; estimation feedback; driven deep; estimation ... See more keywords
Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems With Implicit CSI
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal on Selected Areas in Communications"
DOI: 10.1109/jsac.2022.3196064
Abstract: In an aerial hybrid massive multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) system, how to design a spectral-efficient broadband multi-user hybrid beamforming with a limited pilot and feedback overhead is challenging. To this… read more here.
Keywords: neural network; network; driven deep; data driven ... See more keywords
CSI Feedback With Model-Driven Deep Learning of Massive MIMO Systems
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2021.3138927
Abstract: In order to achieve reliable communication with a high data rate of massive multiple-input multiple-output (MIMO) systems in frequency division duplex (FDD) mode, the estimated channel state information (CSI) at the receiver needs to be… read more here.
Keywords: mimo systems; massive mimo; driven deep; csi feedback ... See more keywords
A Lightweight RL-Driven Deep Unfolding Network for Robust WMMSE Precoding
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2025.3600648
Abstract: Weighted Minimum Mean Square Error (WMMSE) precoding can achieve near-optimal weighted sum rate (WSR) in MU-MIMO-OFDM systems, but it suffers from high computational complexity and severe dependence on accurate channel state information (CSI). This letter… read more here.
Keywords: deep unfolding; driven deep; network; wmmse ... See more keywords
A Data-Driven Deep Neural Network for Modeling of Ionospheric Clutter in HFSWR
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2023.3274203
Abstract: In the practical application of high-frequency surface wave radar (HFSWR), ionospheric clutter has become the principal obstacle to target detection. Since ionospheric clutter has nonlinear and time-varying characteristics, the current suppression methods still require improvement.… read more here.
Keywords: driven deep; deep neural; clutter; hfswr ... See more keywords
Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy
Sign Up to like & getrecommendations! Published in 2024 at "Cancer Research"
DOI: 10.1158/0008-5472.can-23-2040
Abstract: Generation of interpretable and personalized adaptive treatment schedules using a deep reinforcement framework that interacts with a virtual patient model overcomes the limitations of standardized strategies caused by heterogeneous treatment responses. read more here.
Keywords: model driven; deep learning; personalized adaptive; driven deep ... See more keywords
Self-supervised model-driven deep learning for two-step phase-shifting interferometry.
Sign Up to like & getrecommendations! Published in 2025 at "Optics letters"
DOI: 10.1364/ol.577384
Abstract: Data-driven deep learning methods are widely applied in interferometry. However, their performance depends heavily on the quality of the training datasets, which limits both accuracy and generalization. This Letter introduces a model-driven deep-learning approach for… read more here.
Keywords: deep learning; driven deep; phase; model driven ... See more keywords
Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields
Sign Up to like & getrecommendations! Published in 2022 at "PLOS Computational Biology"
DOI: 10.1371/journal.pcbi.1011055
Abstract: Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use… read more here.
Keywords: driven deep; neural networks; pressure velocity; deep neural ... See more keywords
Giant diatom blooms driven by deep water upwelling since late MIS3? Evidence from the rim of the Mariana Trench
Sign Up to like & getrecommendations! Published in 2025 at "Frontiers in Marine Science"
DOI: 10.3389/fmars.2025.1556799
Abstract: Laminated Diatom Mats (LDMs) in the low-latitude Western Pacific provide key insights into global climate and carbon cycling. While Ethmodiscus rex (E. rex) LDMs research has advanced, two critical aspects remain to be elucidated: (1)… read more here.
Keywords: deep water; blooms driven; driven deep; diatom blooms ... See more keywords
Graph-Driven Deep Reinforcement Learning for Vehicle Routing Problems with Pickup and Delivery
Sign Up to like & getrecommendations! Published in 2025 at "Applied Sciences"
DOI: 10.3390/app15094776
Abstract: Recently, the vehicle routing problem with pickup and delivery (VRP-PD) has attracted increasing interest due to its widespread applications in real-life logistics and transportation. However, existing learning-based methods often fail to fully exploit hierarchical graph… read more here.
Keywords: graph driven; vehicle routing; driven deep; reinforcement learning ... See more keywords