In our Journal Club lab members take turns presenting papers they find interesting and optimally others may also find interesting and useful. The Journal Club alternates with our Progress Club where lab members present the state of their research. The target time frame for both formats is 12-15 minutes as that is often what the time slots at conferences are.
Artificial neural networks are universal function approximators. They can forecast dynamics, but they may need impractically many neurons to do so, especially if the dynamics is chaotic. We use neural networks that incorporate Hamiltonian dynamics to efficiently learn phase space orbits even as nonlinear systems transition from order to chaos. We demonstrate Hamiltonian neural networks on a widely used dynamics benchmark, the Hénon-Heiles potential, and on nonperturbative dynamical billiards. We introspect to elucidate the Hamiltonian neural network forecasting.
Presented on 01.07.2020 (Ioannis Iossifidis)