Speaker
Prof.
Andrea Walther
(Humboldt-Universität zu Berlin)
Description
The computation of higher-order derivatives using algorithmic differentiation has been an active area of research for several decades. In recent years, this topic has gained renewed attention due to the growing success of physics-informed neural networks (PINNs), in which such derivatives must be efficiently propagated through corresponding networks.
In this talk, we discuss several methodological approaches for obtaining higher-order derivatives using techniques from algorithmic differentiation. We also highlight emerging research directions that arise from questions related to computational efficiency, numerical stability, and software implementation.