Matrix Product States for Modeling Dynamical Processes on Networks

Explore a cutting-edge approach to modeling dynamical processes on networks in this 39-minute conference talk by Caterina De Bacco from the Max Planck Institute for Intelligent Systems. Delve into the challenges of studying stochastic dynamical processes in homogeneous and heterogeneous networks, and discover how matrix product states combined with dynamic message-passing algorithms can revolutionize computational methods. Learn how this innovative approach reduces computational complexity from exponential to polynomial in both system size and duration, enabling more effective approximations of parallel-update dynamical processes on networks. Gain insights into the limitations of current methods and the potential of this new technique to capture transient dynamics in far-from-equilibrium systems.