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cs.LG2026
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…
cs.LG2026
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…
cs.LG2024
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…