Date of seminar: 12 July 2021 - 4:00pm - 5:00pm (GST) Title: Learning classical and quantum dynamical laws from data Abstract: Traditionally, physical laws are being formulated in a largely heuristic fashion and subsequently their predictions are empirically explored. Generations of physics students are being told that Hamiltonians govern the dynamics of physical systems both in the quantum and classical realm. While this is perfectly right, much less is said on how these Hamiltonian are determined or characterized in the first place. Often, a-priori knowledge of some sort is available, but then the question emerges of how one can be sure that the actual Hamiltonian is close to the anticipated one based on physical reasoning. This issue seems particularly pressing for complex systems involving many degrees of freedom, or for systems in the quantum technologies where high precision is imperative. These basic yet profound insights motivate efforts to learn Hamiltonians - or directly physical laws - from data. In the first part of this talk, we will be concerned with new ways of learning classical dynamical laws from data. We move on to learn instances of quantum Hamiltonians from data, and show how superconducting devices as experimented with by the Google AI team can be characterized to unprecedented precision. We will see how one can set up a tensor network based and machine learning inspired way of learning quantum many-body Hamiltonians from dynamical data. If time allows, I will mention aspects of rigorously minded quantum-assisted machine learning and of the recovery of quantum processes from data. In an outlook, we will discuss further perspectives of data-driven approaches in identifying physical laws from data.