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“A score-based operator Newton construction of a solution to measure transport” Nisha Chandramoorthy

Abstract:  We discuss a new construction of a solution to the measure transport problem. The new construction arises from an infinite-dimensional generalization of a Newton method to find the zero of a "score operator". We define such a score operator that gives the difference of the score -- gradient of logarithm of density -- of a transported distribution from the target score. The new construction is iterative, enjoys fast convergence under smoothness assumptions, and does not make a parametric ansatz on the transport map. It is appropriate for the variational inference setting, where the score is known, and for sampling certain chaotic dynamical systems, where a conditional score can be calculated even in the absence of a statistical model for the target. 

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"Information-theoretic quantification of causality in turbulent flows" Yuenong Ling

Abstract: Understanding causality among quantities of interest in turbulent flows is essential for physical understanding, modeling, and control. In this talk, we discuss a new method to quantify causality in turbulence based on information fluxes. The main properties of the method and its suitability for quantifying causality are highlighted. The method is non-intrusive and only requires the time history of the flow, making it convenient both computationally and experimentally. We leveraged the approach to investigate the causality of the energy cascade in both shell model and homogeneous isotropic turbulence. The results are validated against other causality quantification methods, which involve modifications of the whole system.