31 citations · 36 across the 5 of their papers we have counts for
5 papers
The HydroGym Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, Sajeda Mokbel, Miro Gondrum +18
Modeling and controlling fluids is critical across science and engineering. Effective flow control can increase lift, reduce drag, enhance mixing, and attenuate noise, potentially…
Koopman-Assisted Reinforcement Learning
Preston Rozwood, Edward Mehrez, Ludger Paehler +2
The Bellman equation and its continuous form, the Hamilton-Jacobi-Bellman equation, are ubiquitous in reinforcement learning and control theory. However, these equations become int…
ComPile: A Large IR Dataset from Production Sources
Aiden Grossman, Ludger Paehler, Konstantinos Parasyris +6
Code is increasingly becoming a core data modality of modern machine learning research impacting not only the way we write code with conversational agents like OpenAI's ChatGPT, Go…
On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods
Artur P. Toshev, Ludger Paehler, Andrea Panizza +1
Recent developments in Machine Learning approaches for modelling physical systems have begun to mirror the past development of numerical methods in the computational sciences. In t…
Sparse Identification of Truncation Errors
Stephan Thaler, Ludger Paehler, Nikolaus A. Adams
This work presents a data-driven approach to the identification of spatial and temporal truncation errors for linear and nonlinear discretization schemes of Partial Differential Eq…