Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users

Adam: A Method for Stochastic Optimization

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A Mathematical Theory of Communication

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Gradient-based learning applied to document recognition

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A Mathematical Theory of Communication

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A survey on transfer learning

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On Information and Sufficiency

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Bagging predictors

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Monte Carlo sampling methods using Markov chains and their applications.

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Distilling the Knowledge in a Neural Network

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Classification of Fermi-LAT blazars with Bayesian neural networks

Butter, A., Finke, T., Keil, F., Krämer, M., & Manconi, S. (2022) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2022/04/023 · Cited by 14

Physics-Constrained Bayesian Neural Network for Bias and Variance Reduction

Malashkhia, L., Liu, D., Lu, Y., & Wang, Y. (2022) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4055924 · Cited by 14

Uncertainty explanation of artificial intelligence models by SHAP

Cheng, X., Wang, T., Zhu, D., & Ma, J. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115437 · Cited by 18

ResMon: Domain-Adaptive Wireless Respiration State Monitoring via Few-Shot Bayesian Deep Learning

Zheng, L., Bi, S., Wang, S., Quan, Z., Li, X., Lin, X., & Wang, H. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3284407 · Cited by 13

Reservoir interwell connectivity estimation from small datasets using a probabilistic data driven approach and uncertainty quantification

Thiam, M., & Nakhaee, A. (2023) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2023.212154 · Cited by 13

CiM-BNN:Computing-in-MRAM Architecture for Stochastic Computing Based Bayesian Neural Network

Gu, H., Jia, X., Liu, Y., Yang, J., Wang, X., Zhang, Y., Cotöfană, S., & Zhao, W. S. (2023) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2023.3317136 · Cited by 13

Uncertainty Quantification in Deep Learning Based Kalman Filters

Dahan, Y., Revach, G., Duník, J., & Shlezinger, N. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447987 · Cited by 13

Machine-learning-assisted searching for thermally conductive polymers: A mini review

Hu, Y., Wang, Q., & Ma, H. (2024) · Journal of Applied Physics · doi:10.1063/5.0201613 · Cited by 13

Ensemble Modeling with a Bayesian Maximal Information Coefficient-Based Model of Bayesian Predictions on Uncertainty Data

Surapunt, T., & Wang, S. (2024) · Information · doi:10.3390/info15040228 · Cited by 13

Generative adversarial networks enable outlier detection and property monitoring for additive manufacturing of complex structures

Henkes, A., Herrmann, L., Wessels, H., & Kollmannsberger, S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108993 · Cited by 14

An uncertainty-incorporated active data diffusion learning framework for few-shot equipment RUL prediction

Zhang, C., Gong, D., & Xue, G. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110632 · Cited by 15

Robust crop disease detection using multi-domain data augmentation and isolated test-time adaptation

Fu, R., Jiao, H., Sun, Y., Wang, S., Al-Absi, M. A., Wang, X., & Sun, H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127324 · Cited by 13

Long-term financial predictions based on Feynman–Dirac path integrals, deep Bayesian networks and temporal generative adversarial networks

Soleymani, F., & Paquet, E. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100255 · Cited by 13

Ferroelectric NAND for efficient hardware bayesian neural networks

Song, M. S., Koo, R., Kim, J., Han, C., Yim, J., Ko, J., Yoo, S., Choe, D., Kim, S., Shin, W., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61980-y · Cited by 14

Predictive structural assessment with Bayesian deep learning

Kuhn, S. V., Weber, M., Binggeli, A., Kraus, M. A., Pérez‐Cruz, F., & Kaufmann, W. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103721 · Cited by 12

Machine learning for water quality prediction and uncertainty assessment

Mengistu, T. D., Chung, I.-M., & Chang, S. W. (2026) · Physics and Chemistry of the Earth Parts A/B/C · doi:10.1016/j.pce.2026.104319 · Cited by 12

Hierarchical Bayesian approach to experimental data fusion: Application to strength prediction of high entropy alloys from hardness measurements

Karumuri, S., McClure, Z. D., Strachan, A. H., Titus, M. S., & Bilionis, I. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2022.111851 · Cited by 11

Gradient and uncertainty enhanced sequential sampling for global fit

Lämmle, S., Bogoclu, C., Cremanns, K., & Roos, D. (2023) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2023.116226 · Cited by 13

Approximating the uncertainty of deep learning reconstruction predictions in single-pixel imaging

Shang, R., O’Brien, M. A., Wang, F., Situ, G., & Luke, G. P. (2023) · Communications Engineering · doi:10.1038/s44172-023-00103-1 · Cited by 14

Uncertainty-aware deep learning-based CAD system for breast cancer classification using ultrasound and mammography images

Chegini, M., & Far, A. M. (2023) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2023.2297983 · Cited by 14

Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X-ray Absorption Spectroscopy

Carbone, M. R., Maffettone, P. M., Qu, X., Yoo, S., & Lu, D. (2024) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c06910 · Cited by 12

Inherent Diverse Redundant Safety Mechanisms for AI-Based Software Elements in Automotive Applications

Pitale, M., Abbaspour, A., & Upadhyay, D. K. (2024) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2024-01-2864 · Cited by 12

A Bayesian network for simultaneous keyframe and landmark detection in ultrasonic cine

Feng, Y., Yang, J., Li, M., Tang, L.-Z., Sun, S., & Wang, Y. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103228 · Cited by 12

Shedding light on uncertainties in machine learning: formal derivation and optimal model selection

Corso, G. D., Colantonio, S., & Caudai, C. (2025) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2025.107548 · Cited by 13

Score-Based Diffusion Models With Self-Supervised Learning for Accelerated 3D Multi-Contrast Cardiac MR Imaging

Liu, Y., Cui, Z., Qin, S., Liu, C., Zheng, H., Wang, H., Zhou, Y., Liang, D., & Zhu, Y. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3534206 · Cited by 19

An Interpretable Model With Probabilistic Integrated Scoring for Mental Health Treatment Prediction: Design Study

Kelly, A., Jensen, E. S., Grua, E. M., Mathiasen, K., & van de Ven, P. W. J. (2025) · JMIR Medical Informatics · doi:10.2196/64617 · Cited by 10

Probabilistic photonic computing for AI

Brückerhoff‐Plückelmann, F., Ovvyan, A. P., Varri, A., Borras, H., Klein, B., Meyer, L., Wright, C. D., Bhaskaran, H., Sarwat, S. G., Sebastian, A., et al. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00800-1 · Cited by 16

Few-Shot Quality-Diversity Optimization

Salehi, A., Coninx, A., & Doncieux, S. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3148438 · Cited by 12

Stochastic-Stackelberg-Game-Based Edge Service Selection for Massive IoT Networks

Liang, H. P., & Zhang, W. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3303480 · Cited by 12

Uncertainty quantification of a deep learning fuel property prediction model

Yalamanchi, K. K., Kommalapati, S., Pal, P., Kuzhagaliyeva, N., AlRamadan, A. S., Mohan, B., Pei, Y., Sarathy, S. M., Cenker, E., & Badra, J. (2023) · Applications in Energy and Combustion Science · doi:10.1016/j.jaecs.2023.100211 · Cited by 12

Using constrained-disorder principle-based systems to improve the performance of digital twins in biological systems (Preprint)Preprint

Sigawi, T., & Ilan, Y. (2023) · doi:10.2196/preprints.46304 · Cited by 12

Quantum Computing for Climate Change Detection, Climate Modeling, and Climate Digital TwinPreprint

Otgonbaatar, S., Nurmi, O., Johansson, M. P., Mäkelä, J., kocman, T., Gawron, P., Puchała, Z., Mielzcarek, J., Miroszewski, A., & Dumitru, C. O. (2023) · doi:10.36227/techrxiv.24478663.v1 · Cited by 17

COMPARISON OF UNCERTAINTY QUANTIFICATION METHODS FOR CNN-BASED REGRESSION

Wursthorn, K., Hillemann, M., & Ulrich, M. (2022) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b2-2022-721-2022 · Cited by 11

Uncertainty Quantification of Machine Learning-Based Permeability Estimation from Rock Images

Liu, S., Fan, M., & Lu, D. (2022) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2023.212160 · Cited by 12

Uncertainty-Aware Virtual Sensors for Cyber-Physical Systems

Sen, S., Husom, E. J., Göknil, A., Tverdal, S., & Nguyen, P. H. (2023) · IEEE Software · doi:10.1109/ms.2023.3306873 · Cited by 11

Neural network models for influenza forecasting with associated uncertainty using Web search activity trends

Morris, M., Hayes, P., Cox, I. J., & Lampos, V. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011392 · Cited by 15

Bayesian Strategy Networks Based Soft Actor-Critic Learning

Yang, Q., & Parasuraman, R. N. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3643862 · Cited by 12

DUDES: Deep Uncertainty Distillation using Ensembles for Semantic Segmentation

Landgraf, S., Wursthorn, K., Hillemann, M., & Ulrich, M. (2024) · PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science · doi:10.1007/s41064-024-00280-4 · Cited by 21

Interactive Prediction and Decision-Making for Autonomous Vehicles: Online Active Learning With Traffic Entropy Minimization

Zhang, Y., Lou, S., Hang, P., Huang, W., Yang, L., & Lv, C. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3419003 · Cited by 12

Startup Sustainability Forecasting with Artificial Intelligence

Takas, N., Kouloumpris, E., Moutsianas, K. A., Liapis, G., Vlahavas, I. P., & Kousenidis, D. V. (2024) · Applied Sciences · doi:10.3390/app14198925 · Cited by 11

Accelerated design of solid bio-based foams for plastics substitutes

Miranda-Valdez, I. Y., Mäkinen, T., Coffeng, S., Päivänsalo, A., Jannuzzi, L., Viitanen, L., Koivisto, J., & Alava, M. J. (2024) · Materials Horizons · doi:10.1039/d4mh01464b · Cited by 13

Federated learning meets Bayesian neural network: Robust and uncertainty-aware distributed variational inference

Li, P., Hu, Q., & Wang, X. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107135 · Cited by 12

Uncertainty-guided Bayesian active learning for cost-effective fault diagnosis with minimal labeled data

Chen, Y., Zhao, L., & Han, T. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111945 · Cited by 11

Benchmarking Scalable Predictive Uncertainty in Text Classification

Landeghem, J. V., Blaschko, M. B., Anckaert, B., & Moens, M. (2022) · IEEE Access · doi:10.1109/access.2022.3168734 · Cited by 13

Uncertainty Quantification for Deep Unrolling-Based Computational Imaging

Ekmekci, C., & Çetin, M. (2022) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2022.3233185 · Cited by 11

Binarized multi-gate mixture of Bayesian experts for cardiac syndrome X diagnosis: A clinician-in-the-loop scenario with a belief-uncertainty fusion paradigm

Abdar, M., Mehrzadi, A., Goudarzi, M., Masoudkabir, F., Rundo, L., Mamouei, M., Sala, E., Khosravi, A., Makarenkov, V., Acharya, U. R., et al. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101813 · Cited by 11

Sinh-arcsinh-normal distributions to add uncertainty to neural network regression tasks: Applications to tropical cyclone intensity forecasts

Barnes, E. A., Barnes, R. J., & DeMaria, M. (2023) · Environmental Data Science · doi:10.1017/eds.2023.7 · Cited by 14

Modular Model-Based Bayesian Learning for Uncertainty-Aware and Reliable Deep MIMO Receivers

Raviv, T., Park, S., Simeone, O., & Shlezinger, N. (2023) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops57953.2023.10283687 · Cited by 11

Dual-stage artificial neural network (ANN) model for sequential LBMM-μEDM-based micro-drilling

Noor, W. I., Saleh, T., Rashid, M. A. N., Ibrahim, A. M., & Ali, M. S. M. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-07910-w · Cited by 11

Preface: Advancing deep learning for remote sensing time series data analysis

Zhang, H. K., Camps‐Valls, G., Liang, S., Tuia, D., Pelletier, C., & Zhu, Z. (2025) · Remote Sensing of Environment · doi:10.1016/j.rse.2025.114711 · Cited by 13

Bayesian deep learning for probabilistic aquifer vulnerability and uncertainty prediction

Mengistu, T. D., Kim, M.-G., Chung, I.-M., & Chang, S. W. (2026) · Scientific Reports · doi:10.1038/s41598-025-32612-8 · Cited by 10

A BAYESIAN NEURAL NETWORK APPROACH TO MULTI-FIDELITY SURROGATE MODELING

Kerleguer, B., Cannamela, C., & Garnier, J. C. (2023) · International Journal for Uncertainty Quantification · doi:10.1615/int.j.uncertaintyquantification.2023044584 · Cited by 10

Uncertainty aware neural network from similarity and sensitivity

Kabir, H. M. D., Mondal, S. K., Khanam, S., Khosravi, A., Rahman, S., Qazani, M. R. C., Alizadehsani, R., Asadi, H., Mohamed, S. M. K., Nahavandi, S., et al. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.111027 · Cited by 10

Analysis of data‐driven models for predicting fatigue strength of steel components with uncertainty quantification

Frie, C., Kolyshkin, A., & Eberl, C. (2023) · Fatigue & Fracture of Engineering Materials & Structures · doi:10.1111/ffe.14195 · Cited by 10

Artificial intelligence and machine learning in aerodynamics

Kou, J., & Xiao, T. (2024) · Metascience in Aerospace · doi:10.3934/mina.2024009 · Cited by 10

Uncertainty-based knowledge distillation for Bayesian deep neural network compression

Hemmatian, M., Shahzadi, A., & Mozaffari, S. (2024) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2024.109301 · Cited by 11

Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations

Maksimovic, M., & Maksymov, I. S. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9010012 · Cited by 13

CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks

Wang, K., Shariatmadar, K., Manchingal, S. K., Fabio Cuzzolin, A., Moens, D., & Hallez, H. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107198 · Cited by 10

Prediction and Uncertainty Quantification of the Fatigue Life of Corroded Cable Steel Wires Using a Bayesian Physics-Informed Neural Network

Liu, Z., Kong, X., Deng, L., Peng, H., & Zhang, J. (2025) · Journal of Bridge Engineering · doi:10.1061/jbenf2.beeng-6908 · Cited by 10

Application of Uncertainty to Out-of-Distribution Detection for Autonomous Driving Perception Safety

Wang, K., Ma, Q., Shen, C., & Lǚ, J. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3557996 · Cited by 13

Interplay between Bayesian neural networks and deep learning: A survey

Chen, Y., Yu, S. S., Li, Z., Eshraghian, J. K., & Lim, C. P. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114438 · Cited by 10

UAV-asisted IoT network framework with hybrid deep reinforcement and federated learning

Andreou, A. S., Mavromoustakis, C. X., Markakis, E., Bourdena, A., & Mastorakis, G. N. (2025) · Scientific Reports · doi:10.1038/s41598-025-21014-5 · Cited by 10

Data Rate Aware Reliable Transmission Mechanism in Wireless Sensor Networks using Bayesian Regularized Neural Network approach

Pundir, M., Sandhu, J. K., Gupta, D., Gadekallu, T. R., Juneja, A., Gulzar, Y., & Nauman, A. (2023) · Physical Communication · doi:10.1016/j.phycom.2023.102115 · Cited by 10

Making It Tractable to Catch Duplicates and Conflicts in Graphs

Fan, W., Fu, W., Jin, R., Liu, M., Lü, P., & Tian, C. (2023) · Proceedings of the ACM on Management of Data · doi:10.1145/3588940 · Cited by 10

Investigations on machine learning-based control-oriented modeling using historical thermal data of buildings

Zhang, Q., Huang, Y., Chng, C.-B., Chui, C., & Lee, P. S. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110595 · Cited by 11

Uncertainty Analysis of Deep Kernel Learning Methods on Diabetic Retinopathy Grading

Siebert, M., Graßhoff, J., & Rostalski, P. (2023) · IEEE Access · doi:10.1109/access.2023.3343642 · Cited by 14

Bayesian polynomial neural networks and polynomial neural ordinary differential equations

Fronk, C., Yun, J.-W., Singh, P., & Petzold, L. R. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012414 · Cited by 11

Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic ModelsPreprint

Chang, D. T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.00120 · Cited by 10

Uncertainty Quantification-Based Multi-Objective Optimization Design of Electrical Machines Using Probabilistic Metamodels

Chen, N., Digel, C., & Doppelbauer, M. (2024) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2024.3454755 · Cited by 9

Uncertainty quantification for a deep learning models for image-based crack segmentation

dos Santos, K. R. M., Chassignet, A. G. J., Pantoja-Rosero, B. G., Rezaie, A. H., Savary, O., & Beyer, K. (2024) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-024-00879-6 · Cited by 9

Evidential Semantic Mapping in Off-road Environments with Uncertainty-aware Bayesian Kernel Inference

Kim, J., Seo, J., & Min, J. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802766 · Cited by 9

Probabilistic modeling of explosibility of low reactivity dusts

Alauddin, M., Addo, A., Khan, F. I., & Amyotte, P. R. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.110861 · Cited by 9

Predicting Stress–Strain Curve with Confidence: Balance Between Data Minimization and Uncertainty Quantification by a Dual Bayesian Model

Li, T., Chen, Z., Zhang, Z., Wei, Z., Zhong, G., Li, Z., & Liu, H. (2025) · Polymers · doi:10.3390/polym17040550 · Cited by 9

ITF-VAE: Variational Auto-Encoder Using Interpretable Continuous Time Series Features

Klopries, H., & Schwung, A. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3545396 · Cited by 9

Uncertainty-Aware Parking Prediction Using Bayesian Neural Networks

Nezhadettehad, A., Zaslavsky, A., Rakib, A., & Loke, S. W. (2025) · Sensors · doi:10.3390/s25113463 · Cited by 10

Active-learning-driven error control for data-driven state of charge estimation across the lithium battery lifecycle

Xue, J., Du, X., Zhao, L., Yang, Z., Xia, C., Ma, Y., Hoque, M. J., Fu, W., Yan, X., & Miljkovic, N. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100549 · Cited by 9

Bayesian deep learning based semantic segmentation for unmanned surface vehicles in uncertain marine environments

Ye, Z., Huang, Y., Wu, P., & Liu, Y. (2025) · Ocean Engineering · doi:10.1016/j.oceaneng.2025.122065 · Cited by 9

Stochastic fundamental diagram modeling of mixed traffic flow: A data-driven approach

Zhang, X., Yang, K., Sun, J., & Sun, J. (2025) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2025.105279 · Cited by 9

Financial Time Series Uncertainty: A Review of Probabilistic AI Applications

Eggen, S., Espe, T. J., Grude, K. V., Risstad, M., & Sandberg, R. (2025) · Journal of Economic Surveys · doi:10.1111/joes.70018 · Cited by 17

Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors

Wang, D., Wu, Z., Shen, C., Bao, L., Luo, H., Wang, Z., Yao, H., De, K., Luo, C., & Hou, T. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac592 · Cited by 9

Vehicle-to-Everything (V2X) Datasets for Machine Learning-Based Predictive Quality of Service

Skocaj, M., Cicco, N. D., Zugno, T., Boban, M., Blumenstein, J., Prokeš, A., Mikulášek, T., Vychodil, J., Mikhaylov, K., Tornatore, M., et al. (2023) · IEEE Communications Magazine · doi:10.1109/mcom.004.2200723 · Cited by 9

Towards algorithms and models that we can trust: A theoretical perspective

Oneto, L., Ridella, S., & Anguita, D. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127798 · Cited by 12

How to break the mass sheet degeneracy with the light curves of microlensed Type Ia supernovae

Weisenbach, L. W., Collett, T. E., de Murieta, A. S., Krawczyk, C., Vernardos, G., Enzi, W., & Lundgren, A. P. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1396 · Cited by 9

PI3NN: Out-of-distribution-aware prediction intervals from three neural networksPreprint

Liu, S., Zhang, P., Lu, D., & Zhang, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.02327 · Cited by 9

Data Coverage Assessment on Neural Network Based Digital Twins for Autonomous Control SystemPreprint

Wang, L., Lin, L., & Dinh, N. T. (2022) · SSRN Electronic Journal · doi:10.1016/j.anucene.2022.109568 · Cited by 9

An Uncertainty-Aware Health Monitoring Model for Wind Turbine Drivetrains Based On Bayesian Neural Network

Amin, A., Bibo, A., Panyam, M., & Tallapragada, P. (2023) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2023.12.030 · Cited by 9

Bayesian Deep Learning via Expectation Maximization and Turbo Deep Approximate Message Passing

Xu, W., Liu, A., Zhang, Y., & Lau, V. K. N. (2024) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2024.3442858 · Cited by 8

An improved heuristic dynamic programming framework for multi-source frequency regulation

Zhao, Z., Yang, X., Chen, H., & Chen, H. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125623 · Cited by 8

An Integrated Strategy for Interpretable Fault Diagnosis of UAV EHA DC Drive Circuits Under Early Fault and Imbalanced Data Conditions

Li, Y., Jia, Z. Y., Liu, J., Wang, K., Zhao, P., Liu, X., & Liu, Z. (2025) · Drones · doi:10.3390/drones9030189 · Cited by 8

Transforming Neural Networks into Quantum-Cognitive Models: A Research Tutorial with Novel Applications

Maksimovic, M., & Maksymov, I. S. (2025) · Technologies · doi:10.3390/technologies13050183 · Cited by 14

On the Potential of Bayesian Neural Networks for Estimating Chlorophyll-a Concentration from Satellite Data

Hammoud, M. A. E. R., Papagiannopoulos, N., Krokos, G., Brewin, R. J. W., Raitsos, D. Ε., Knio, O., & Hoteit, I. (2025) · Remote Sensing · doi:10.3390/rs17111826 · Cited by 8

Accelerating Materials Discovery Through Sparse Gaussian Process Machine Learning Potentials

Ha, M., Pourasad, S., Myung, C. W., & Kim, K. S. (2025) · Accounts of Chemical Research · doi:10.1021/acs.accounts.5c00667 · Cited by 10

Perspectives on Systematic Cloud Microphysics Scheme Development With Machine Learning

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