Recent Developments of the Particle Swarm Optimization Algorithm. - Schalk Kok; Daniel N. Wilke; Albert A. Groenwold (Computational Intelligence, 2005)

Reference frame and scale invariant real-parameter genetic and differential evolution algorithms. - Daniel N. Wilke; Schalk Kok; Albert A. Groenwold (GECCO, 2007)

On rotationally invariant continuous-parameter genetic algorithms. - Marthinus N. Ras; Daniel N. Wilke; Albert A. Groenwold; Schalk Kok (Adv. Eng. Softw., 2014)

Development of a convex polyhedral discrete element simulation framework for NVIDIA Kepler based GPUs. - Nicolin Govender; Daniel N. Wilke; Schalk Kok; Rosanne Els (J. Comput. Appl. Math., 2014)

Collision detection of convex polyhedra on the NVIDIA GPU architecture for the discrete element method. - Nicolin Govender; Daniel N. Wilke; Schalk Kok (Appl. Math. Comput., 2015)

Blaze-DEMGPU: Modular high performance DEM framework for the GPU architecture. - Nicolin Govender; Daniel N. Wilke; Schalk Kok (SoftwareX, 2016)

A study of shape non-uniformity and poly-dispersity in hopper discharge of spherical and polyhedral particle systems using the Blaze-DEM GPU code. - Nicolin Govender; Daniel N. Wilke; Patrick Pizette; Nor-Edine Abriak (Appl. Math. Comput., 2018)

Spatially distributed statistical significance approach for real parameter tuning with restricted budgets. - Adolph J. Vogel; Daniel N. Wilke (Appl. Soft Comput., 2018)

On the rotational variance of the differential evolution algorithm. - Marthinus N. Ras; Daniel N. Wilke; Albert A. Groenwold; Schalk Kok (Adv. Eng. Softw., 2019)

Visual interpretation of the robustness of Non-Negative Associative Gradient Projection Points over function minimizers in mini-batch sampled loss functions. - Dominic Kafka; Daniel N. Wilke (CoRR, 2019)

Gradient-only line searches: An Alternative to Probabilistic Line Searches. - Dominic Kafka; Daniel N. Wilke (CoRR, 2019)

Empirical study towards understanding line search approximations for training neural networks. - Younghwan Chae; Daniel N. Wilke (CoRR, 2019)

Investigating the interaction between gradient-only line searches and different activation functions. - Dominic Kafka; Daniel N. Wilke (CoRR, 2020)

Gradient-only line searches to automatically determine learning rates for a variety of stochastic training algorithms. - Dominic Kafka; Daniel Nicolas Wilke (CoRR, 2020)

GOALS: Gradient-Only Approximations for Line Searches Towards Robust and Consistent Training of Deep Neural Networks. - Younghwan Chae; Daniel N. Wilke; Dominic Kafka (CoRR, 2021)

Traction chain networks: Insights beyond force chain networks for non-spherical particle systems. - Daniel N. Wilke (CoRR, 2021)

An empirical study into finding optima in stochastic optimization of neural networks. - Dominic Kafka; Daniel N. Wilke (Inf. Sci., 2021)

Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches. - Dominic Kafka; Daniel N. Wilke (J. Glob. Optim., 2021)

Application of Anti-Diagonal Averaging in Response Reconstruction. - Bradley Dean Collins; P. Stephan Heyns; Schalk Kok; Daniel N. Wilke (Symmetry, 2021)

Parametric Circuit Fault Diagnosis Through Oscillation-Based Testing in Analogue Circuits: Statistical and Deep Learning Approaches. - Jacob B. Cloete; Tinus Stander; Daniel N. Wilke (IEEE Access, 2022)

On the momentum diffusion over multiphase surfaces with meshless methods. - Johannes C. Joubert; Daniel N. Wilke; Patrick Pizette (CoRR, 2023)

A Novel and Fully Automated Domain Transformation Scheme for Near Optimal Surrogate Construction. - Johann Bouwer; Daniel N. Wilke; Schalk Kok (CoRR, 2023)

Latent Space Perspicacity and Interpretation Enhancement (LS-PIE) Framework. - Jesse Stevens; Daniel N. Wilke; Itumeleng Setshedi (CoRR, 2023)

A spectral regularisation framework for latent variable models designed for single channel applications. - Ryan Balshaw; P. Stephan Heyns; Daniel N. Wilke; Stephan Schmidt 005 (CoRR, 2023)

Gradient-only surrogate to resolve learning rates for robust and consistent training of deep neural networks. - Younghwan Chae; Daniel N. Wilke; Dominic Kafka (Appl. Intell., 2023)

Multifidelity Surrogate Models: A New Data Fusion Perspective. - Daniel N. Wilke (CoRR, 2024)

Generalised envelope spectrum-based signal-to-noise objectives: Formulation, optimisation and application for gear fault detection under time-varying speed conditions. - Stephan Schmidt 005; Daniel N. Wilke; Konstantinos C. Gryllias (CoRR, 2024)

Spectrally regularised LVMs: A spectral regularisation framework for latent variable models designed for single-channel applications. - Ryan Balshaw; P. Stephan Heyns; Daniel N. Wilke; Stephan Schmidt 005 (SoftwareX, 2025)

Design for Sensing and Digitalisation (DSD): A Modern Approach to Engineering Design. - Daniel N. Wilke (CoRR, 2025)

Towards scientific machine learning for granular material simulations - challenges and opportunities. - Marc Fransen; Andreas Fürst; Deepak Tunuguntla; Daniel N. Wilke; Benedikt Alkin; Daniel Barreto; Johannes Brandstetter; Miguel Angel Cabrera; Xinyan Fan; Mengwu Guo; Bram Kieskamp; Krishna Kumar; et al. (CoRR, 2025)