The physics of optical computing

There has been a resurgence of interest in optical computing over the past decade, both in academia and in industry, with much of the excitement centered around special-purpose optical computers for neural-network processing. Optical computing has been a topic of periodic study for over 50 years, including for neural networks three decades ago, and a wide variety of optical-computing schemes and architectures have been proposed. In this paper we provide a systematic explanation of why and how optics might be able to give speed or energy-efficiency benefits over electronics for computing, enumerating 11 features of optics that can be harnessed when designing an optical computer. One often-mentioned motivation for optical computing -- that the speed of light $c$ is fast -- is not a key differentiating physical property of optics for computing; understanding where an advantage could come from is more subtle. We discuss how gaining an advantage over state-of-the-art electronic processors will likely only be achievable by careful design that harnesses more than one of the 11 features, while avoiding a number of pitfalls that we describe.Comment: 31 pages; 11 figure

Computer simulation of mass transport in groundwater : effect of macroscopic heterogeneities in hydraulic conductivity

In this study a computer model was used to simulate dissolved chloride movement through alluvial sediments which border the Canadian River in Hutchinson County, Texas. Hydraulic conductivity values of the sediments were required in order to calculate groundwater velocities in the system. The most realistic representation of conductivity variations in porous media is expressed by frequency distributions rather than by averaged values of conductivity. Numerous sedimentological environments exhibit log-normal conductivity distributions; therefore, one was used in this investigation. A number of conclusions can be based on the results of this study. First, certain conductivity distributions account for the observed spread of chloride in the aquifer. The best match of observed chloride dispersion was obtained with autocorrelated log-normal conductivity distributions. Secondly, the degree of spatial dependence between adjacent conductivity values affected numerous results. These include the amount of chloride dispersion and the extent of uncertainty in calculated hydraulic head and chloride distributions. For comparative purposes the chloride distribution was also modeled using an average conductivity value. Under this condition the chloride plume moved at an average rate of 10 meters/year. Another result was that longitudinal and transverse dispersivities of 46 meters and 9 meters, respectively, were required to obtain a match between observed and modeled chloride distributions.Geological Science