Speaker
Description
Nonequilibrium phenomena in quantum materials represent an exciting research frontier, in which theoretical insights are critical for both understanding cutting-edge experiments and guiding the exploration and realization of transient quantum states. I will briefly review the main challenges for practical simulations of realistic condensed matter systems based on the propagation of many-body correlation functions, which directly relate to observables, but are hindered by the high dimensionality and temporal non-locality of many-body interactions. The formalism is, however, uniquely positioned to leverage new developments in numerical and AI-enabled techniques. In this talk, I will illustrate several approaches based on dynamic mode decomposition and operator learning methods. They drastically accelerate the nonequilibrium Green’s function dynamics, transforming the computationally expensive functional forms of the system evolution into efficient surrogate models with linear temporal scaling. These approaches, along with new theoretical advances, enable real-time prediction of observables. I will further outline new avenues for AI-driven solvers that retain physical interpretability and adaptability, and the possibility of their integration into new simulation frameworks.
