20 citations · 50 across the 5 of their papers we have counts for
3 papers · 1 filter
Quantifying Local Model Validity using Active Learning
Sven Lämmle, Can Bogoclu, Robert Voßhall +2
Real-world applications of machine learning models are often subject to legal or policy-based regulations. Some of these regulations require ensuring the validity of the model, i.e…
Gradient and Uncertainty Enhanced Sequential Sampling for Global Fit
Sven Lämmle, Can Bogoclu, Kevin Cremanns +1
Surrogate models based on machine learning methods have become an important part of modern engineering to replace costly computer simulations. The data used for creating a surrogat…
Local Latin Hypercube Refinement for Multi-objective Design Uncertainty Optimization
Can Bogoclu, Dirk Roos, Tamara Nestorović
Optimizing the reliability and the robustness of a design is important but often unaffordable due to high sample requirements. Surrogate models based on statistical and machine lea…