Search Results for author:
Found 5 papers, 2 papers with code
Error Estimation for Physics-informed Neural Networks Approximating Semilinear Wave Equations
This paper provides rigorous error bounds for physics-informed neural networks approximating the semilinear wave equation.
Learning-based adaption of robotic friction models
Subsequently, to adapt to more complex asymmetric settings, we train a second network on a small dataset, focusing on predicting the residual of the initial network's output.
Reliable AI: Does the Next Generation Require Quantum Computing?
The cause of these computability problems is rooted in the fact that digital hardware is based on the computing model of the Turing machine, which is inherently discrete.
A Fractional Graph Laplacian Approach to Oversmoothing
In this paper, we generalize the concept of oversmoothing from undirected to directed graphs.
Well-definedness of Physical Law Learning: The Uniqueness Problem
Finally, we provide extensive numerical experiments showing that our algorithms in combination with common approaches for learning physical laws indeed allow to guarantee that a unique governing differential equation is learnt, without assuming any knowledge about the function, thereby ensuring reliability.
