Deep Fluids: A Generative Network for Parameterized Fluid Simulations

This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. Due to the capability of deep learning architectures to learn representative features of the data, our generative model is able to accurately approximate the training data set, while providing plausible interpolated in-betweens. The proposed generative model is optimized for fluids by a novel loss function that guarantees divergence-free velocity fields at all times. In addition, we demonstrate that we can handle complex parameterizations in reduced spaces, and advance simulations in time by integrating in the latent space with a second network. Our method models a wide variety of fluid behaviors, thus enabling applications such as fast construction of simulations, interpolation of fluids with different parameters, time re-sampling, latent space simulations, and compression of fluid simulation data. Reconstructed velocity fields are generated up to 700x faster than re-simulating the data with the underlying CPU solver, while achieving compression rates of up to 1300x.Comment: Computer Graphics Forum (Proceedings of EUROGRAPHICS 2019), additional materials: http://www.byungsoo.me/project/deep-fluids

Multi-code Multicarrier CDMA: Performance Analysis

A new multi-code multicarrier code division multiple access (MC-MC-CDMA) system is proposed and analyzed in a frequency selective fading channel. By allowing each user to transmit an M-ary code sequence, the proposed MC-MC-CDMA system can support various data rates as required by next generation standards without increasing the interference which is common in general multicarrier CDMA systems. The bit error rate of the system is analytically derived in frequency selective fading, with Gaussian noise and multiple access interference. The results show that the proposed MC-MC-CDMA system clearly outperforms both single-code multicarrier CDMA (MC CDMA) and single-carrier multi-code CDMA in a fixed bandwidth allocation. This indicates that MC-MC-CDMA can be considered for next generation cellular systems

Physically-based simulation of ice formation

The geometric and optical complexity of ice has been a constant source of wonder and inspiration for scientists and artists. It is a defining seasonal characteristic, so modeling it convincingly is a crucial component of any synthetic winter scene. Like wind and fire, it is also considered elemental, so it has found considerable use as a dramatic tool in visual effects. However, its complex appearance makes it difficult for an artist to model by hand, so physically-based simulation methods are necessary. In this dissertation, I present several methods for visually simulating ice formation. A general description of ice formation has been known for over a hundred years and is referred to as the Stefan Problem. There is no known general solution to the Stefan Problem, but several numerical methods have successfully simulated many of its features. I will focus on three such methods in this dissertation: phase field methods, diffusion limited aggregation, and level set methods. Many different variants of the Stefan problem exist, and each presents unique challenges. Phase field methods excel at simulating the Stefan problem with surface tension anisotropy. Surface tension gives snowflakes their characteristic six arms, so phase field methods provide a way of simulating medium scale detail such as frost and snowflakes. However, phase field methods track the ice as an implicit surface, so it tends to smear away small-scale detail. In order to restore this detail, I present a hybrid method that combines phase fields with diffusion limited aggregation (DLA). DLA is a fractal growth algorithm that simulates the quasi-steady state, zero surface tension Stefan problem, and does not suffer from smearing problems. I demonstrate that combining these two algorithms can produce visual features that neither method could capture alone. Finally, I present a method of simulating icicle formation. Icicle formation corresponds to the thin-film, quasi-steady state Stefan problem, and neither phase fields nor DLA are directly applicable. I instead use level set methods, an alternate implicit front tracking strategy. I derive the necessary velocity equations for level set simulation, and also propose an efficient method of simulating ripple formation across the surface of the icicles

Developing an acoustic sensing yarn for health surveillance in a military setting

Overexposure to high levels of noise can cause permanent hearing disorders, which have a significant adverse effect on the quality of life of those affected. Injury due to noise can affect people in a variety of careers including construction workers, factory workers, and members of the armed forces. By monitoring the noise exposure of workers, overexposure can be avoided and suitable protective equipment can be provided. This work focused on the creation of a noise dosimeter suitable for use by members of the armed forces, where a discrete dosimeter was integrated into a textile helmet cover. In this way the sensing elements could be incorporated very close to the ears, providing a highly representative indication of the sound level entering the body, and also creating a device that would not interfere with military activities. This was achieved by utilising commercial microelectromechanical system microphones integrated within the fibres of yarn to create an acoustic sensing yarn. The acoustic sensing yarns were fully characterised over a range of relevant sound levels and frequencies at each stage in the yarn production process. The yarns were ultimately integrated into a knitted helmet cover to create a functional acoustic sensing helmet cover prototype

The Supreme Court Forecasting Project: Legal and Political Science Approaches to Predicting Supreme Court Decision-Making

This Essay reports the results of an interdisciplinary project comparing political science and legal approaches to forecasting Supreme Court decisions. For every argued case during the 2002 Term, we obtained predictions of the outcome prior to oral argument using two methods—one a statistical model that relies on general case characteristics, and the other a set of independent predictions by legal specialists. The basic result is that the statistical model did better than the legal experts in forecasting the outcomes of the Term’s cases: The model predicted 75% of the Court’s affirm/reverse results correctly, while the experts collectively got 59.1% right. These results are notable, given that the statistical model disregards information about the specific law or facts of the cases. The model’s relative success was due in large part to its ability to predict more accurately the important votes of the moderate Justices (Kennedy and O’Connor) at the center of the current Court. The legal experts, by contrast, did best at predicting the votes of the more ideologically extreme Justices, but had difficulty predicting the centrist Justices. The relative success of the two methods also varied by issue area, with the statistical model doing particularly well in forecasting “economic activity” cases, while the experts did comparatively better in the “judicial power” cases. In addition to reporting the results in detail, the Essay explains the differing methods of prediction used and explores the implications of the findings for assessing and understanding Supreme Court decision-making.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116230/1/columbia04.pd

Level degeneracy and temperature-dependent carrier distributions in self-organized quantum dots

Using femtosecond three-pulse pump–probe spectroscopy, we investigated the transparency condition for the ground and first excited states in self-organized In0.4Ga0.6AsIn0.4Ga0.6As quantum dots at different temperatures and wavelengths. The temperature-dependent behavior of the transparency condition is consistent with calculations using a multilevel model with a large density of states in the quantum-well reservoir. The twofold spatial degeneracy of the first excited state and the temperature dependence of the thermal equilibrium processes were experimentally observed. © 2003 American Institute of Physics.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/71141/2/APPLAB-82-12-1959-1.pd