Learning partially observed stochastic dynamical systems
Date:
Minisymposium presentation at SIAM Conference on Mathematics of Data Science (MDS22). The slides can be found here.
Dynamical systems abound in engineering and science, and their accurate long-time simulation and outer-loop applications such as control, design and uncertainty quantification remains a computational challenge. From first principles modeling of physical systems, it is clear that many of these dynamical systems have a natural geometric structure (e.g., Hamiltonian, Lagrangian, metriplectic) or symmetry (translational, rotational). Exploiting and enforcing this structure in physics-based learning methods remains imperative for capturing the underlying physics accurately. This minisymposium highlights recent developments in physics-preserving learning for dynamical systems, such as: Lagrangian/Hamiltonian neural networks, sparse identification of nonlinear dynamics (SINDy), operator inference, preservation of conservation laws, the incorporation of interconnection and modular structure, structure-preserving system identification and other machine learning approaches.