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Daniel N. Wilke
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2020 – today
- 2024
- [i14]Daniel N. Wilke:
Multifidelity Surrogate Models: A New Data Fusion Perspective. CoRR abs/2404.14456 (2024) - [i13]Stephan Schmidt, Daniel N. Wilke, Konstantinos C. Gryllias:
Generalised envelope spectrum-based signal-to-noise objectives: Formulation, optimisation and application for gear fault detection under time-varying speed conditions. CoRR abs/2405.00727 (2024) - 2023
- [j12]Younghwan Chae
, Daniel N. Wilke, Dominic Kafka:
Gradient-only surrogate to resolve learning rates for robust and consistent training of deep neural networks. Appl. Intell. 53(11): 13741-13762 (2023) - [i12]Johannes C. Joubert, Daniel N. Wilke, Patrick Pizette
On the momentum diffusion over multiphase surfaces with meshless methods. CoRR abs/2303.09978 (2023) - [i11]Johann Bouwer, Daniel N. Wilke, Schalk Kok:
A Novel and Fully Automated Domain Transformation Scheme for Near Optimal Surrogate Construction. CoRR abs/2303.17694 (2023) - [i10]Jesse Stevens, Daniel N. Wilke, Itumeleng Setshedi:
Latent Space Perspicacity and Interpretation Enhancement (LS-PIE) Framework. CoRR abs/2307.05620 (2023) - [i9]Ryan Balshaw, P. Stephan Heyns, Daniel N. Wilke, Stephan Schmidt:
A spectral regularisation framework for latent variable models designed for single channel applications. CoRR abs/2310.19246 (2023) - 2022
- [j11]Jacob B. Cloete
Parametric Circuit Fault Diagnosis Through Oscillation-Based Testing in Analogue Circuits: Statistical and Deep Learning Approaches. IEEE Access 10: 15671-15680 (2022) - 2021
- [j10]Dominic Kafka, Daniel N. Wilke:
An empirical study into finding optima in stochastic optimization of neural networks. Inf. Sci. 560: 235-255 (2021) - [j9]Dominic Kafka
Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches. J. Glob. Optim. 79(1): 111-152 (2021) - [j8]Bradley Dean Collins
Application of Anti-Diagonal Averaging in Response Reconstruction. Symmetry 13(7): 1165 (2021) - [i8]Younghwan Chae, Daniel N. Wilke, Dominic Kafka:
GOALS: Gradient-Only Approximations for Line Searches Towards Robust and Consistent Training of Deep Neural Networks. CoRR abs/2105.10915 (2021) - [i7]Daniel N. Wilke:
Traction chain networks: Insights beyond force chain networks for non-spherical particle systems. CoRR abs/2106.03771 (2021) - 2020
- [i6]Dominic Kafka, Daniel N. Wilke
Resolving learning rates adaptively by locating Stochastic Non-Negative Associated Gradient Projection Points using line searches. CoRR abs/2001.05113 (2020) - [i5]Dominic Kafka, Daniel N. Wilke
Investigating the interaction between gradient-only line searches and different activation functions. CoRR abs/2002.09889 (2020) - [i4]Dominic Kafka, Daniel Nicolas Wilke
Gradient-only line searches to automatically determine learning rates for a variety of stochastic training algorithms. CoRR abs/2007.01054 (2020)
2010 – 2019
- 2019
- [j7]Marthinus N. Ras, Daniel N. Wilke
On the rotational variance of the differential evolution algorithm. Adv. Eng. Softw. 136 (2019) - [i3]Dominic Kafka, Daniel N. Wilke
Visual interpretation of the robustness of Non-Negative Associative Gradient Projection Points over function minimizers in mini-batch sampled loss functions. CoRR abs/1903.08552 (2019) - [i2]Dominic Kafka, Daniel N. Wilke
Gradient-only line searches: An Alternative to Probabilistic Line Searches. CoRR abs/1903.09383 (2019) - [i1]Younghwan Chae, Daniel N. Wilke
Empirical study towards understanding line search approximations for training neural networks. CoRR abs/1909.06893 (2019) - 2018
- [j6]Nicolin Govender
A study of shape non-uniformity and poly-dispersity in hopper discharge of spherical and polyhedral particle systems using the Blaze-DEM GPU code. Appl. Math. Comput. 319: 318-336 (2018) - [j5]Adolph J. Vogel, Daniel N. Wilke
Spatially distributed statistical significance approach for real parameter tuning with restricted budgets. Appl. Soft Comput. 70: 648-664 (2018) - 2016
- [j4]Nicolin Govender
Blaze-DEMGPU: Modular high performance DEM framework for the GPU architecture. SoftwareX 5: 62-66 (2016) - 2015
- [j3]Nicolin Govender
Collision detection of convex polyhedra on the NVIDIA GPU architecture for the discrete element method. Appl. Math. Comput. 267: 810-829 (2015) - 2014
- [j2]Marthinus N. Ras, Daniel N. Wilke
On rotationally invariant continuous-parameter genetic algorithms. Adv. Eng. Softw. 78: 52-59 (2014) - [j1]Nicolin Govender
Development of a convex polyhedral discrete element simulation framework for NVIDIA Kepler based GPUs. J. Comput. Appl. Math. 270: 386-400 (2014)
2000 – 2009
- 2007
- [c2]Daniel N. Wilke
Reference frame and scale invariant real-parameter genetic and differential evolution algorithms. GECCO 2007: 1538 - 2005
- [c1]Schalk Kok, Daniel N. Wilke, Albert A. Groenwold:
Recent Developments of the Particle Swarm Optimization Algorithm. Computational Intelligence 2005: 392-397
Coauthor Index
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