1 citations · 1 across the 8 of their papers we have counts for
8 papers
Instrumented data for causal scientific machine learning
Daniel N. Wilke
Scientific machine learning is limited less by model size than by the data it is trained on. Observational data records what happened but not why; template synthetic data has a kno…
From Perception to Autonomous Computational Modeling: A Multi-Agent Approach
Daniel N. Wilke
We present a solver-agnostic framework in which coordinated large language model (LLM) agents autonomously execute the complete computational mechanics workflow, from perceptual da…
Towards a data-scale independent regulariser for robust sparse identification of non-linear dynamics
Jay Raut, Daniel N. Wilke, Stephan Schmidt
Data normalisation, a common and often necessary preprocessing step in engineering and scientific applications, can severely distort the discovery of governing equations by magnitu…
Towards scientific machine learning for granular material simulations -- challenges and opportunities
Marc Fransen, Andreas Fürst, Deepak Tunuguntla +21
Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights…
Design for Sensing and Digitalisation (DSD): A Modern Approach to Engineering Design
Daniel N. Wilke
This paper introduces Design for Sensing and Digitalisation (DSD), a new engineering design paradigm that integrates sensor technology for digitisation and digitalisation from the…
Generalised envelope spectrum-based signal-to-noise objectives: Formulation, optimisation and application for gear fault detection under time-varying speed conditions
Stephan Schmidt, Daniel N. Wilke, Konstantinos C. Gryllias
In vibration-based condition monitoring, optimal filter design improves fault detection by enhancing weak fault signatures within vibration signals. This process involves optimisin…
Multifidelity Surrogate Models: A New Data Fusion Perspective
Daniel N Wilke
Multifidelity surrogate modelling combines data of varying accuracy and cost from different sources. It strategically uses low-fidelity models for rapid evaluations, saving computa…
A spectral regularisation framework for latent variable models designed for single channel applications
Ryan Balshaw, P. Stephan Heyns, Daniel N. Wilke +1
Latent variable models (LVMs) are commonly used to capture the underlying dependencies, patterns, and hidden structure in observed data. Source duplication is a by-product of the d…