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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.CE2026
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…
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…