Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Harder, Hans"'
We utilize extreme-learning machines for the prediction of partial differential equations (PDEs). Our method splits the state space into multiple windows that are predicted individually using a single model. Despite requiring only few data points (in
Externí odkaz:
http://arxiv.org/abs/2404.18530
On the continuity and smoothness of the value function in reinforcement learning and optimal control
Autor:
Harder, Hans, Peitz, Sebastian
The value function plays a crucial role as a measure for the cumulative future reward an agent receives in both reinforcement learning and optimal control. It is therefore of interest to study how similar the values of neighboring states are, i.e., t
Externí odkaz:
http://arxiv.org/abs/2403.14432
Autor:
Peitz, Sebastian, Harder, Hans, Nüske, Feliks, Philipp, Friedrich, Schaller, Manuel, Worthmann, Karl
The Koopman operator has become an essential tool for data-driven analysis, prediction and control of complex systems. The main reason is the enormous potential of identifying linear function space representations of nonlinear dynamics from measureme
Externí odkaz:
http://arxiv.org/abs/2307.15325
Boolean functions and their representation through logics, circuits, machine learning classifiers, or binary decision diagrams (BDDs) play a central role in the design and analysis of computing systems. Quantifying the relative impact of variables on
Externí odkaz:
http://arxiv.org/abs/2305.08103
Witnessing subsystems for probabilistic reachability thresholds in discrete Markovian models are an important concept both as diagnostic information on why a property holds, and as input to refinement algorithms. We present SWITSS, a tool for the com
Externí odkaz:
http://arxiv.org/abs/2008.04049