Director dynamics in liquid-crystal physical gels

Nematic liquid-crystal (LC) elastomers and gels have a rubbery polymer network coupled to the nematic director. While LC elastomers show a single, non-hydrodynamic relaxation mode, dynamic light-scattering studies of self-assembled liquid-crystal gels reveal orientational fluctuations that relax over a broad time scale. At short times, the relaxation dynamics exhibit hydrodynamic behavior. In contrast, the relaxation dynamics at long times are non-hydrodynamic, highly anisotropic, and increase in amplitude at small scattering angles. We argue that the slower dynamics arise from coupling between the director and the physically associated network, which prevents director orientational fluctuations from decaying completely at short times. At long enough times the network restructures, allowing the orientational fluctuations to fully decay. Director dynamics in the self-assembled gels are thus quite distinct from those observed in LC elastomers in two respects: they display soft orientational fluctuations at short times, and they exhibit at least two qualitatively distinct relaxation processes

Some Physical Aspects of Liouville String Dynamics

We discuss some physical aspects of our Liouville approach to non-critical strings, including the emergence of a microscopic arrow of time, effective field theories as classical ``pointer'' states in theory space, $CPT$ violation and the possible apparent non-conservation of angular momentum. We also review the application of a phenomenological parametrization of this formalism to the neutral kaon system.Comment: CERN-TH.7269/94, 37 pages, 2 figures (not included), latex. Direct inquiries to: [email protected]

Multi-scale time-stepping in molecular dynamics

We introduce a modified molecular dynamics algorithm that allows one to freeze the dynamics of parts of a physical system, and thus concentrate the simulation effort on selected, central degrees of freedom. This freezing, in contrast to other multi-scale methods, introduces no approximations in the thermodynamic behaviour of the non-central variables while conserving the Newtonian dynamics of the region of physical interest.Comment: two figures, one tabl

Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images

In this paper, we study the challenging problem of predicting the dynamics of objects in static images. Given a query object in an image, our goal is to provide a physical understanding of the object in terms of the forces acting upon it and its long term motion as response to those forces. Direct and explicit estimation of the forces and the motion of objects from a single image is extremely challenging. We define intermediate physical abstractions called Newtonian scenarios and introduce Newtonian Neural Network ($N^3$) that learns to map a single image to a state in a Newtonian scenario. Our experimental evaluations show that our method can reliably predict dynamics of a query object from a single image. In addition, our approach can provide physical reasoning that supports the predicted dynamics in terms of velocity and force vectors. To spur research in this direction we compiled Visual Newtonian Dynamics (VIND) dataset that includes 6806 videos aligned with Newtonian scenarios represented using game engines, and 4516 still images with their ground truth dynamics