20 citations · 50 across the 5 of their papers we have counts for
5 papers
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…
Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy Search
Can Bogoclu, Robert Vosshall, Kevin Cremanns +1
Probabilistic world models increase data efficiency of model-based reinforcement learning (MBRL) by guiding the policy with their epistemic uncertainty to improve exploration and a…
Intelligent Optimization and Machine Learning Algorithms for Structural Anomaly Detection using Seismic Signals
Maximilian Trapp, Can Bogoclu, Tamara Nestorović +1
The lack of anomaly detection methods during mechanized tunnelling can cause financial loss and deficits in drilling time. On-site excavation requires hard obstacles to be recogniz…
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…