Daniël N. Wilke

University of the Witwatersrand — South Africa

Engineering · Physical Sciences

24h-index2.3kcitations170works1.42yr avg
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Research Topics

Granular flow and fluidized beds(24), Mineral Processing and Grinding(18), Advanced Multi-Objective Optimization Algorithms(16), Ultrasonics and Acoustic Wave Propagation(15), Machine Fault Diagnosis Techniques(14)

Publications170 total

Towards Scientific Machine Learning for Granular Material Simulations: Challenges and Opportunities
Archives of Computational Methods in Engineering·2025· 2 citedOpen Access
Verifying hyperparameter sensitivities of optimal filter design methods for fault signature enhancement
Springer Link (Chiba Institute of Technology)·2025
Spectrally regularised LVMs: A spectral regularisation framework for latent variable models designed for single-channel applications
SoftwareX·2025Open Access
Towards scientific machine learning for granular material simulations -- challenges and opportunities
arXiv (Cornell University)·2025Open Access
Design for Sensing and Digitalisation (DSD): A Modern Approach to Engineering Design
arXiv (Cornell University)·2025Open Access
Verifying hyperparameter sensitivities of optimal filter design methods for fault signature enhancement
Mechanics & Industry·2025Open Access
Challenges and Research Needs in the Offer Phase of Special-Purpose Machinery and Plant Engineering: A Qualitative Study With Industry Experts
IEEE Engineering Management Review·2024Open Access
Generalised envelope spectrum-based signal-to-noise objectives: Formulation, optimisation and application for gear fault detection under time-varying speed conditions
Mechanical Systems and Signal Processing·2024· 6 citedOpen Access
Latent Space Perspicacity and Interpretation Enhancement (LS-PIE) Framework
Mathematical and Computational Applications·2024· 1 citedOpen Access
Enhancing LS-PIE’s Optimal Latent Dimensional Identification: Latent Expansion and Latent Condensation
Mathematical and Computational Applications·2024Open Access
Temporally-preserving latent variable models: Offline and online training for reconstruction and interpretation of fault data for gearbox condition monitoring
Journal of Dynamics Monitoring and Diagnostics·2024· 4 citedOpen Access
Generalised envelope spectrum-based signal-to-noise objectives: Formulation, optimisation and application for gear fault detection under time-varying speed conditions
arXiv (Cornell University)·2024Open Access
Feature detection in guided wave ultrasound measurements using simulated spectrograms and generative machine learning
NDT & E International·2024· 11 cited
A Systematic Study of the Effect of Signal Alignment in Information Extraction from Railway Infrastructure Recording Vehicle Data
Lecture notes in mechanical engineering·2024
Robust Optimization of Discontinuous Loss Functions
2024
Robust Optimization of Discontinuous Loss Functions
2024
Towards a Railway Infrastructure Digital Twin Framework for African Railway Lifecycle Management
Lecture notes in mechanical engineering·2024
Improving Optimization-based Inverse Analysis using Direct Inverse Maps: A Dynamic Damage Identification Case Study
Acta Polytechnica Hungarica·2024· 3 citedOpen Access
Effects of Rail-Guided Wave Spectrogram Training Sample Information on Interpretative Models' Latent Spaces and Reconstruction Errors
SSRN Electronic Journal·2024Open Access
A STOCHASTIC MODEL TO QUANTIFY AND OPTIMIZE THE IMPACT OF OPERATIONAL RISKS ON CORPORATE SUSTAINABILITY USING MONTE CARLO SIMULATION
Polish Journal of Management Studies·2023Open Access
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Frequent Co-authors

N. Govender(21), Schalk Kok(13), Patrick Pizette(9), P. Stephan Heyns(7), Albert A. Groenwold(7), Philip W. Loveday(6), Chuan‐Yu Wu(5), N.-E. Abriak(4), Raj K. Rajamani(4), Johannes Khinast(4), Wen-Jie Xu(4), Dineo A. Ramatlo(4), Craig S. Long(3), Stephan Schmidt(3), J. A. Snyman(2), Benjamin J. Glasser(2), Hermann Kureck(2), U. Tüzün(2), Guangyu Liu(2), Isaac I. Setshedi(2)