A5087888904- Climate variability and models
- Meteorological Phenomena and Simulations
- Model Reduction and Neural Networks
- Solar Radiation and Photovoltaics
- Computational Physics and Python Applications
- Atmospheric and Environmental Gas Dynamics
- Speech and dialogue systems
- Advanced Computational Techniques and Applications
- AI in Service Interactions
- Social Robot Interaction and HRI
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
2023-2024
Utrecht University
2021
Abstract Climate models are essential to understand and project climate change, yet long‐standing biases uncertainties in their projections remain. This is largely associated with the representation of subgrid‐scale processes, particularly clouds convection. Deep learning can learn these processes from computationally expensive storm‐resolving while retaining many features at a fraction computational cost. Yet, simulations embedded neural network parameterizations still challenging highly...
Robot-assisted language learning (RALL) is a promising application when employing social robots to help both children and adults acquire an increasingly widely studied area of child–robot interaction. By introducing prosodic entrainment, i.e., converging the robot’s pitch with that learner, present study aimed provide new insights into RALL as facilitative method for interactive tutoring. It hypothesized pitch-level entrainment by Nao robot during word task in foreign will result increased...
Earth system models are fundamental to understanding and projecting climate change, although there considerable biases uncertainties in their projections. A large contribution this uncertainty stems from differences the representation of clouds convection occurring at scales smaller than resolved model grid. These long-standing deficiencies cloud parameterizations have motivated developments computationally costly global high-resolution resolving models, that can explicitly resolve...
Climate models are essential to understand and project climate change, yet long-standing biases uncertainties in their projections remain. This is largely associated with the representation of subgrid-scale processes, particularly clouds convection. Deep learning can learn these processes from computationally expensive storm-resolving while retaining many features at a fraction computational cost. Yet, simulations embedded neural network parameterizations still challenging highly depend on...