Browsing by Author "Forootaninia, Zahra"

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    Guiding simulations of highly dynamic phenomena
    (2022-08) Forootaninia, Zahra
    Highly dynamic phenomena, such as fluid flow or the motion of a crowd, are hard topredict and control since their behavior varies dramatically with a small perturbation in their initial or environmental conditions. Computer simulations let us recreate these dynamic systems and design techniques to control their behavior based on our desired goal. In my work, I have developed techniques that efficiently modify the dynamics of a complex system to achieve the desired motion. This can be achieved by changing the local or global dynamics of the system to control its small- or large-scale behaviors, respectively. Part of my work focuses on utilizing a statistical-mechanical model governing pedestrian motion for multi-agent navigation. I developed two specific models that account for uncertainty in the future trajectories of interacting agents: an isotropic model which conservatively considers all possible errors and an anisotropic model that assumes the error is only in a direction toward a head-on collision. I compare the two models experimentally via a number of simulation scenarios, and also provide theoretical guarantees about the collision avoidance behavior of the agents considering the uncertainties in the sensing data each agent receives. In my more recent work, I propose a simple and efficient method for guiding an Eulerian smoke simulation to match the behavior of a specified velocity field, such as a low-resolution animation of the same scene, while preserving the rich, turbulent details arising in the simulated fluid. The method works by simply combining the high-frequency component of the simulated fluid velocity with the low-frequency component of the input guiding field. I provide a frequency-domain analysis that motivates the use of ideal low-pass and high-pass filters to prevent the artificial dissipation of small-scale details. I demonstrate this method in many scenes including those with static and moving obstacles and show that it produces high-quality results with very little computational overhead. Following my last guiding technique for smoke simulation, in my last project, I proposed a machine learning model for the same problem that learns the desired behavior of the smoke from low-resolution simulated data and generates the high-resolution smoke. I utilized a generative adversarial network with the recurrency of frames in the network. The trained model is capable of maintaining the spatial and temporal consistency of simulation. Compare to the physics-based guiding model, the machine-learning model can generate guided smoke interactively. Although, it can not still beat the quality that the frequency-domain guiding model can produce.
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    The radio luminosity function and galaxy evolution of Abell 2256
    (2015-01) Forootaninia, Zahra
    This thesis presents a study of the radio luminosity function and the evolution of galaxies in the Abell 2256 cluster (z=0.058, richness class 2). Using the NED database and VLA deep data with an rms sensitivity of 18$\mu$ Jy.$beam^{-1}$, we identified 257 optical galaxies as members of A2256, of which 83 are radio galaxies. Since A2256 is undergoing a cluster-cluster merger, it is a good candidate to study the radio activity of galaxies in the cluster. We calculated the Univariate and Bivariate radio luminosity functions for A2256, and compared the results to studies on other clusters. We also used the SDSS parameter fracDev to roughly classify galaxies as spirals and ellipticals, and investigated the distribution and structure of galaxies in the cluster.We found that most of the radio galaxies in A2256 are faint, and are distributed towards the outskirts of the cluster. On the other hand, almost all very bright radio galaxies are ellipticals which are located at the center of the cluster. We also found there is an excess in the number of radio spiral galaxies in A2256 compared to the number of radio ellipticals, counting down to a radio luminosity of $log(luminosity)=20.135$ $W/Hz$.