8 papers
When is a System Discoverable from Data? Discovery Requires Chaos
Zakhar Shumaylov, Peter Zaika, Philipp Scholl +3
The deep learning revolution has spurred a rise in advances of using AI in sciences. Within physical sciences the main focus has been on discovery of dynamical systems from observa…
Improved probabilistic regression using diffusion models
Carlo Kneissl, Christopher Bülte, Philipp Scholl +1
Probabilistic regression models the entire predictive distribution of a response variable, offering richer insights than classical point estimates and directly allowing for uncerta…
Interpretable Robotic Friction Learning via Symbolic Regression
Philipp Scholl, Alexander Dietrich, Sebastian Wolf +4
Accurately modeling the friction torque in robotic joints has long been challenging due to the request for a robust mathematical description. Traditional model-based approaches are…
Learning-based adaption of robotic friction models
Philipp Scholl, Maged Iskandar, Sebastian Wolf +5
In the Fourth Industrial Revolution, wherein artificial intelligence and the automation of machines occupy a central role, the deployment of robots is indispensable. However, the m…
ParFam -- (Neural Guided) Symbolic Regression Based on Continuous Global Optimization
Philipp Scholl, Katharina Bieker, Hillary Hauger +1
The problem of symbolic regression (SR) arises in many different applications, such as identifying physical laws or deriving mathematical equations describing the behavior of finan…
Graph Neural Networks for Enhancing Ensemble Forecasts of Extreme Rainfall
Christopher Bülte, Sohir Maskey, Philipp Scholl +2
Climate change is increasing the occurrence of extreme precipitation events, threatening infrastructure, agriculture, and public safety. Ensemble prediction systems provide probabi…