O Dürr, B Sick, E Murina - 2020
Probabilistic Deep Learning is a hands-on guide to the principles that support neural
networks. Learn to improve network performance with the right distribution for different data
types, and discover Bayesian variants that can state their own uncertainty to increase
accuracy. This...
M Arpogaus, M Voß, B Sick, M Nigge-Uricher, O Dürr - ICML 2021, Workshop Tackling Climate Change with Machine …, 2021
Abstract: The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level. However, high fluctuations and increasing electrification cause...
O Dürr, T Volz, W Dieterich, A Nitzan - The Journal of chemical physics, 2002
Abstract: Tracer-diffusion of small molecules through dense systems of chain polymers is studied
within an athermal lattice model, where hard-core interactions are taken into account by
means of the site exclusion principle. An...
O Dürr, W Dieterich, A Nitzan - The Journal of chemical physics, 2004
Abstract: Monte Carlo simulations are used to study ion and polymer chain dynamic properties in a
simplified lattice model with only one species of mobile ions. The ions interact attractively
with specific beads in the...
W Dieterich, O Dürr, P Pendzig, A Bunde, A Nitzan - Physica A: Statistical Mechanics and its Applications, 1999 - Elsevier
Abstract: Structural disorder is an inherent property of solid materials, which can support a macroscopic ionic current. Many transport phenomena in these solid ionic conductors...
O Dürr, F Duval, A Nichols, P Lang, A Brodte, S Heyse… - SLAS Discovery, 2007 - Elsevier
Abstract: Recent technological advances in high-content screening instrumentation have increased its ease of use and throughput, expanding the application of high-content screening to the early stages...