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Efficient Bounds and Estimates for Canonical Angles in Randomized Subspace Approximations
Not provided.
Multiscale Neural Networks for Approximating Green’s Functions
Neural networks (NNs) have been widely used to solve partial differential equations (PDEs) in the applications of physics, biology, and engineering. One effective approach for solving PDEs with a fixed differential operator is learning Green’s functions. However, Green’s functions are notoriously difficult to learn due to their poor regularity, which typically requires larger NNs and longer training times. In this work, we address these challenges by leveraging multiscale NNs to learn Green’s functions. Through theoretical analysis using multiscale Barron space methods and experimental validation, we show that the multiscale approach significantly reduces the necessary NN size and accelerates training.
Quantum Fast Approximate Synthesis Tool (QFAST) v1.0.0
We present QFAST, a quantum synthesis tool designed to produce short circuits and to scale well in practice. Our contributions are: 1) a novel representation of circuits able to encode placement and topology; 2) a hierarchical approach with an iterative refinement formulation that combines "coarse-grained" fast optimization during circuit structure search with a good, but slower, optimization stage only in the final circuit instantiation stage. When compared against state-of-the-art techniques, although not optimal, QFAST can generate much shorter circuits for "time dependent evolution" algorithms used by domain scientists. We also show the composability and tunability of our formulation in terms of circuit depth and running time. For example, we show how to generate shorter circuits by plugging in the best available third party synthesis algorithm at a given hierarchy level. Composability enables portability across chip architectures, which is missing from the available approaches.
FINITE-ELEMENT DISTORED WAVE BORN APPROXIMATION FOR SURFACE SCATTERING
FE-DWBA is a software package that computes the x-ray scattering patterns of objects that locate on a surface or buried next to a surface, such as the nano-circuits and nano-patterns of the mask used in the extreme ultraviolet lithography. It can incorporate the dynamical scattering effect (photons scattered multiple times), which is predominant in surface scattering and yield high-fidelity scattering patterns. The application includes surface imaging using x-rays, and critical dimension determination for semiconductor manufacturing.
Super-Cell Random APproximates(SCRAPs): A Multicomponent Alloy Structure Design Tool
A novel Hybrid CUCKOO SEARCH determines combinatorial global optimum SCRAPs for specified point and pair correlations in high-entropy alloys having proper distributions.
Data for Approximately 15% of Miscanthus Yield is Lost at Current Commercial Cutting Heights in Iowa
Various works have quantitatively characterized the effects of environmental and management factors on Miscanthus x giganteus Greef et Deu ( mxg ) yield and, therefore, anticipated land requirement per unit production. However, little work has addressed the effects of cutting height, which may significantly contribute to the difference between the standing aboveground biomass at harvest (i.e., biological yield) and harvested yield. This study quantitatively characterized the effect of cutting height using a replicated nitrogen trial of a 5-year-old mxg stand in southeast Iowa and related this information to observations of cutting height in nearby commercial fields. Nitrogen fertilizer did not significantly change the relationship of the stem segment mass to length, and overall, a 1-cm stem segment contributes 0.5% of the total stem biomass within the bottom 44 cm of the stem. This results in an average harvest loss of 15% of the aboveground standing biomass when cutting at 30 cm, typically seen in commercial mxg fields in eastern Iowa. Cutting height should be considered when accurately predicting commercial mxg harvest yields and changes in soil organic carbon in a commercial mxg agroecosystem.
Incorporating the effects of objects in an approximate model of light transport in scattering media
A computationally efficient radiative transport model is presented that predicts a camera measurement and accounts for the light reflected and blocked by an object in a scattering medium. The model is in good agreement with experimental data acquired at the Sandia National Laboratory Fog Chamber Facility (SNLFC). The model is applicable in computational imaging to detect, localize, and image objects hidden in scattering media. Here, a statistical approach was implemented to study object detection limits in fog.
SURROGATE BASED MUTUAL INFORMATION APPROXIMATION AND OPTIMIZATION FOR URBAN SOURCE LOCALIZATION
Not provided.
HePHAESTUS: Heat Pipe Heterogeneous Approximation of Energy and Navier-Stokes Equations, Transience, and Non-Uniform Surface Heating [Slides]
A heat pipe is a liquid/vapor filled metal pipe capable of transporting large amounts of heat through evaporation of a liquid at one end and condensation of vapor at the other end creating a closed fluid loop. Heat pipes are widely used in the cooling of electronics and in space applications. Heat pipes are typically composed of a pipe wall, a wick material, a working fluid, and a central vapor channel. Heat is transferred into the working fluid at the “evaporator” end, through an adiabatic section, and heat is removed at the opposite end at the “condenser.” If no wick is used, gravitational acceleration can circulate the fluid due to the buoyancy of the vapor in the central core and a falling film of liquid at the pipe wall forming a “thermosyphon."
Biologically Inspired Robust Perception Approximating Sparse Coding [Slides]
Sparse coding has a variety of applications, such as image denoising and data compression. Recent work has shown the how sparse coding and biologically inspired architectures can mitigate adversarial attacks on images.
CIS Project 22359, Final Technical Report. Discretized Posterior Approximation in High Dimensions
Our primary aim in this work is to understand how to efficiently obtain reliable uncertainty quantification in automatic learning algorithms with limited training datasets. Standard approaches rely on cross-validation to tune hyper parameters. Unfortunately, when our datasets are too small, holdout datasets become unreliable—albeit unbiased—measures of prediction quality due to the lack of adequate sample size. We should not place confidence in holdout estimators under conditions wherein the sample variance is both large and unknown. More poigniantly, our training experiments on limited data (Duersch and Catanach, 2021) show that even if we could improve estimator quality under these conditions, the typical training trajectory may never even encounter generalizable models.
Approximating and incorporating model uncertainty in an inversion for seismic source functions: Preliminary results
We present preliminary work on propagating model uncertainty into the estimation of the time domain source time functions of the seismic source. Our method is based on an estimated model covariance function, which we estimate from the data. The model covariance function is then used to construct a suite of surrogate Greens functions which we use in a Monte Carlo type inversion scheme. The result is a probability density function of the six independent source time functions, each of which corresponds to an individual component of the seismic moment tensor. We compare the results of our method with those obtained using a computationally expensive finite difference Monte Carlo method and find that our new method produces results that are deficient in low frequencies. The advantage of our new method, which we term the Karhunen-Loeve Monte Carlo (KLMC) method, is that is several orders of magnitude faster than our current method, which uses a finite difference scheme to produce the suite of forward models.
Geometric Tail Approximation for Reliability and Survivability
A common problem in developing high-reliability systems is estimating the reliability for a population of components that cannot be 100% tested. The radiation survivability of a population of components is often estimated by testing a very small sample to some multiple of the required specification level, known as an overtest. Given a successful test with a sufficient overtest margin, the population of components is assumed to have the required survivability or radiation reliability. However, no mathematical justification for such claims has been crafted without making aggressive assumptions regarding the statistics of the unknown distribution. Here we illustrate a new approach that leverages geometric bounding arguments founded on relatively modest distribution assumptions to produce conservative estimates of component reliability.
Approximating Power System Dynamics for Black Start Planning.
Abstract not provided.
Meshless Methods for Manifolds GMLS Approximations of Hydrodynamic Responses in Curved Fluid Interfaces.
Abstract not provided.