Current status of double differential cross section measurement for antineutrino DIS vs A at MINERvA
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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
SEARCH · Engineering Papers
Results for “Differentiable physics”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Chromatin structures from integrated AI and polymer physics model
The physical organization of the genome in three-dimensional space regulates many biological processes, including gene expression and cell differentiation. Three-dimensional characterization of genome structure is critical to understanding these biological processes. Direct experimental measurements of genome structure are challenging; computational models of chromatin structure are therefore necessary. We develop an approach that combines a particle-based chromatin polymer model, molecular simulation, and machine learning to efficiently and accurately estimate chromatin structure fromindirectmeasures of genome structure. More specifically, we introduce a new approach where the interaction parameters of the polymer model are extracted from experimental Hi-C data using a graph neural network (GNN). We train the GNN on simulated data from the underlying polymer model, avoiding the need for large quantities of experimental data. The resulting approach accurately estimates chromatin structures across all chromosomes and across several experimental cell lines despite being trained almost exclusively on simulated data. The proposed approach can be viewed as a general framework for combining physical modeling with machine learning, and it could be extended to integrate additional biological data modalities. Ultimately, we achieve accurate and high-throughput estimations of chromatin structure from Hi-C data, which will be necessary as experimental methodologies, such as single-cell Hi-C, improve.
Biochemistry & Molecular Biology↗
Ion Number Density Quantification Utilizing Pulsing Frequency in Negative Differential Resistance (NDR) Regime of Microplasma Operation
A mathematical model is presented for quantifying the ion number density over a range of discharge current in microplasma discharges. The ion number density is determined from measured discharge voltage, current, pulsing frequency, and trace impurity that is injected into the system deliberately. Experiments are conducted in pure helium with nitrogen being introduced as the trace species. The ion number density is estimated over a range of discharge current and is compared with multidimensional simulation results.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing
Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques lags behind their development in the lab. This means that material and process development proceeds largely via trial and error. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to leverage machine learning's unparalleled pattern matching capability to pursue data-driven experimental design. DPC takes two possible experiment parameter sets and outputs a prediction of which will produce a material with a more desirable property specified by the operator. We demonstrate the success of DPC on AA7075 tube manufacturing process and mechanical property data using shear assisted processing and extrusion (ShAPE), an emerging solid phase processing technology. We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.
advanced manufacturing, machine learning, ShAPE↗
Linear embedding of nonlinear dynamical systems and prospects for efficient quantum algorithms
The simulation of large nonlinear dynamical systems, including systems generated by discretization of hyperbolic partial differential equations, can be computationally demanding. Such systems are important in both fluid and kinetic computational plasma physics. This motivates exploring whether a future error-corrected quantum computer could perform these simulations more efficiently than any classical computer. In this work, we describe a method for mapping any finite nonlinear dynamical system to an infinite linear dynamical system (embedding) and detail three specific cases of this method that correspond to previously studied mappings. Then we explore an approach for approximating the resulting infinite linear system with finite linear systems (truncation). Using a number of qubits only logarithmic in the number of variables of the nonlinear system, a quantum computer could simulate truncated systems to approximate output quantities if the nonlinearity is sufficiently weak. Other aspects of the computational efficiency of the three detailed embedding strategies are also discussed.
97 MATHEMATICS AND COMPUTING↗
A Comparison of Emerging Nonfluoropolymer-Based Coextruded PV Backsheets to Industry-Benchmark Technologies
As the photovoltaic (PV) industry is rapidly expanding around the world, there has been an increasing interest in extending the lifespan of PV modules. Concern has also emerged regarding the recyclability of modules and their component materials, including fluoropolymer-based backsheets. Laminated polyethylene-terephthalate (PET) core backsheets have traditionally been used in the PV industry, but new, coextruded polyolefin (PO) backsheets show promise as an improved alternative. In this work, minimodule and coupon samples of seven different backsheets (made of layers including contemporary PET and fluoropolymers, novel PO, and polyamide materials) were run through hygrometric- or UV photolytic-accelerated aging to identify and better understand each material's degradation modes and the backsheets' field reliability. In addition to the artificial aging, the natural weathering methods used in this article are described. The comprehensive set of chemical, mechanical, and structural characterizations at intermittent read points in this article is presented, including: visual appearance and color; gloss; mechanical tensile testing; I-V performance; electroluminescence (EL) imaging; dielectric breakdown; Fourier-transform infrared-chemical structure; X-ray-polymer structure; and differential scanning calorimetry-crystalline content. After 4000 h of aging, a strong correlation occurs between initial physical characteristics (mechanical tensile test) and operating performance (EL and I-V characteristics).
14 SOLAR ENERGY↗
Variability of intensity ratios of H to He and He to ions with Z not smaller than 3 in solar energetic particle events
Data from the solid-state detector on Explorer 35 are applied to a study of two intensity ratios in the sub-MeV per nucleon specific kinetic energy range for several energetic particle events. It is found that the intensity ratios vary markedly from event to event, particularly during the time history of the individual events. This implies that the ratios have no simple relationship to 'solar abundances' in the usual sense of the term. The pattern of the variability of each ratio is established; the ratio of He to ions with Z not smaller than 3 starts with a low value and increases as the event proceeds. The H/He ratio exhibits a qualitatively similar time history with marked relative enhancement of He early in an event. Differential diffusion of the various ionic species with differing magnetic rigidities is seen to be the dominant physical cause for the variabilities observed.
Van Allen, J. A.↗
Non-Gaussian approach for parametric random vibration of non-linear structures
The dynamic response of a nonlinear, single degree of freedom structural system subjected to a physically white noise parametric excitation is investigated. The Ito stochastic calculus is employed to derive a general differential equation for the moments of the response coordinates. The differential equations of moments of any order are found to be coupled with higher order moments. A non-Gaussian closure scheme is developed to truncate the moment equations up to fourth order. The statistical of the stationary response are computed numerically and compared with analytical solutions predicted by a Gaussian closure scheme and the stochastic averaging method. It is found that the computed results exhibit the jump phenomenon which is typical of the characteristics of deterministic nonlinear systems. In addition, the numerical algorithm leads to multiple solutions all of which give positive mean squares. However, two of these solutions are found to violate the properties of high order moments. One solution preserves the moments properties and demonstrates that the system achieves a stationary response.
Ibrahim, R. A.↗
Three-dimensional grids as solutions of elliptic systems
An elliptic grid generation scheme is described which produces a curvilinear, boundary-fitted grid for highly swept wings with sharp leading edges. The three-dimensional integration domain is segmented in successive crossflow sections wherein the physical coordinates are transformed to computational coordinates by solving an elliptic set of two quasi-linear differential equations. Grid points are clustered in the vicinity of the wing surface, especially in the neighborhood of the leading edges. The formulation of the source terms in the equations governing the transformation contains adjustable parameters which are determined from limiting forms of the elliptic equations along the boundaries. The source terms are assigned to each grid point by interpolation of these parameters into the interior domain. The formulation does not depend on the boundary shape or on the distribution of the grid points along the boundaries. For the first time, the discretized governing equations have been solved using a fast AF1 iteration scheme.
Hartwich, P.-M.↗
Three-Axis Superconducting Gravity Gradiometer
Gravity gradients measured even on accelerating platforms. Three-axis superconducting gravity gradiometer based on flux quantization and Meissner effect in superconductors and employs superconducting quantum interference device as amplifier. Incorporates several magnetically levitated proof masses. Gradiometer design integrates accelerometers for operation in differential mode. Principal use in commercial instruments for measurement of Earth-gravity gradients in geo-physical surveying and exploration for oil.
Paik, Ho Jung↗
Continuous system modeling
A comprehensive and systematic introduction is presented for the concepts associated with 'modeling', involving the transition from a physical system down to an abstract description of that system in the form of a set of differential and/or difference equations, and basing its treatment of modeling on the mathematics of dynamical systems. Attention is given to the principles of passive electrical circuit modeling, planar mechanical systems modeling, hierarchical modular modeling of continuous systems, and bond-graph modeling. Also discussed are modeling in equilibrium thermodynamics, population dynamics, and system dynamics, inductive reasoning, artificial neural networks, and automated model synthesis.
Cellier, Francois E.↗
Combustion noise and combustion instabilities in propulsion systems
This paper is concerned with some aspects of non-linear behavior of unsteady motions in combustion chambers. The emphasis is on conditions under which organized oscillations having discrete frequencies may exist in the presence of random motions. In order to treat the two types of motions together, and particularly to investigate coupling between noise and combustion instabilities, the unsteady field is represented as a synthesis of acoustic modes having time-varying amplitudes. Each of the amplitudes are written as the sum of two parts, one associated with the random field and the remainder representing the organized oscillations. After spatial averaging, the general problem is reduced to solution of a set of second-order ordinary differential equations whose structure depends on the sorts of nonlinear processes accounted for. This formulation accommodates any physical process; in particular, terms are included to represent noise sources, although only limited modeling is discussed. Our results suggest that random sources of noise have only small effects on combustion instabilities and seem not to be a cause of unstable motions. However, the coupling between the two sorts of unsteady motions may be important as an essential process in a proposed scheme for noise control. It is now a familiar observation that many nonlinear deterministic systems are capable of exhibiting apparently random motions called 'chaos.' This is a particularly interesting possibility for systems which also executed non-deterministic random motions. In combustion chambers, a nonlinear deterministic system (acoustical motions) exists in the presence of noise produced by flow separation, turbulent motions, and energy released by combustion processes. The last part of the paper is directed to the matter of discovering whether or not chaotic motions exist in combustion systems. Analysis has not progressed sufficiently far to answer the question. We report here recent results of processing data taken in one combustor to determine the dimensions of any attractors in the motions. No evidence has been found for chaos in the strict sense, but the method seems to be an important means of investigating the nonlinear behavior of combustion systems.
F. E. C. Culick↗
Intercomparison of single-frequency methods for retrieving a vertical rain profile from airborne or spaceborne radar data
This paper briefly reviews several single-frequency rain profiling methods for an airborne or spaceborne radar. The authors describe the different methods from a unified point of view starting from the basic differential equation. This facilitates the comparisons between the methods and also provides a better understanding of the physical and mathematical basis of the methods. The application of several methods to airborne radar data taken during the Convective and Precipitation/Electrification Experiment is shown. Finally, the authors consider a hybrid method that provides a smooth transition between the Hitschfeld-Bordan method, which performs well at low attenuations, and the surface reference method, for which the relative error decreases with increasing path attenuation.
Iguchi, Toshio↗
Model-Reference Adaptive Control of Distributed Lagrangian Infinite-Dimensional Systems Using Hamilton’s Principle
This paper presents a Hamilton's principle for distributed control of infinite-dimensional systems modeled by a distributed form of the Euler-Lagrange method. The distributed systems are governed by a system of linear partial differential equations in space and time. A generalized potential energy expression is developed that can capture most physical systems including those systems that have no spatial distribution. The Hamilton's principle is applied to derive distributed feedback control methods without resorting to the standard weak-form discretization approach to convert an infinite-dimensional systems to a finite-dimensional systems. It can be shown by the principle of least action that the distributed control synthesized by the Hamilton's principle is a minimum-norm control. A model-reference adaptive control framework is developed for distributed Lagrangian systems in the presence of uncertainty. The theory is demonstrated by an application of adaptive flutter suppression control of a flexible aircraft wing.
Nguyen, Nhan T.↗
Solving differential equations using deep neural networks
Recent work on solving partial differential equations (PDEs) with deep neural networks (DNNs) is presented. The paper reviews and extends some of these methods while carefully analyzing a fundamental feature in numerical PDEs and nonlinear analysis: irregular solutions. First, the Sod shock tube solution to the compressible Euler equations is discussed and analyzed. This analysis includes a comparison of a DNN-based approach with conventional finite element and finite volume methods, and demonstrates that the DNN is competitive in terms of degrees of freedom required for a given accuracy. Further, the DNN-based approach is extended to consider performance improvements and simultaneous parameter space exploration. Next, a shock solution to compressible magnetohydrodynamics (MHD) is solved for, and used in a scenario where experimental data is utilized to enhance a PDE system that is a priori insufficient to validate against the observed/experimental data. This is accomplished by enriching the model PDE system with source terms that are then inferred via supervised training with synthetic experimental data. The resulting DNN framework for PDEs enables straightforward system prototyping and natural integration of large data sets (be they synthetic or experimental), all while simultaneously enabling single-pass exploration of an entire parameter space.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Improving Solution Accuracy and Convergence for Stochastic Physics Parameterizations with Colored Noise
Stochastic parameterizations are used in numerical weather prediction and climate modeling to help capture the uncertainty in the simulations and improve their statistical properties. Convergence issues can arise when time integration methods originally developed for deterministic differential equations are applied naively to stochastic problems. In previous studies, it has been demonstrated that a correction term, known in stochastic analysis as the Itô correction, can help improve solution accuracy for various deterministic numerical schemes and ensure convergence to the physically relevant solution without substantial computational overhead. The usual formulation of the Itô correction is valid only when the stochasticity is represented by white noise. In this study, a generalized formulation of the Itô correction is derived for noises of any color. The formulation is applied to a test problem described by an advection–diffusion equation forced with a spectrum of fast processes. We present numerical results for cases with both constant and spatially varying advection velocities to show that, for the same time step sizes, the introduction of the generalized Itô correction helps to substantially reduce time integration error and significantly improve the convergence rate of the numerical solutions when the forcing term in the governing equation is rough (fast varying); alternatively, for the same target accuracy, the generalized Itô correction allows for the use of significantly longer time steps and, hence, helps to reduce the computational cost of the numerical simulation.
54 ENVIRONMENTAL SCIENCES↗
A Molecular Description of Hydrogel Forming Polymers for Cement-Based Printing Paste Applications
This research endeavors to link the physical and chemical characteristics of select polymer hydrogels to differences in printability when used as printing aids in cement-based printing pastes. A variety of experimental probes including differential scanning calorimetry (DSC), NMR-diffusion ordered spectroscopy (DOSY), quasi-elastic neutron scattering (QENS) using neutron backscattering spectroscopy, and X-ray powder diffraction (XRD), along with molecular dynamic simulations, were used. Conjectures based on objective measures of printability and physical and chemical-molecular characteristics of the polymer gels are emerging that should help target printing aid selection and design, and mix formulation. Molecular simulations were shown to link higher hydrogen bond probability and larger radius of gyration to higher viscosity gels. Furthermore, the higher viscosity gels also produced higher elastic properties, as measured by neutron backscattering spectroscopy.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Quantum algorithm for the linear Vlasov equation with collisions
The Vlasov equation is a nonlinear partial differential equation that provides a first-principles description of the dynamics of plasmas. Its linear limit is routinely used in plasma physics to investigate plasma oscillations and stability. In this paper, we present a quantum algorithm that simulates the linearized Vlasov equation with and without collisions, in the one-dimensional electrostatic limit. Rather than solving this equation in its native spatial and velocity phase space, we adopt an efficient representation in the dual space yielded by a Fourier-Hermite expansion. For a given simulation time, the Fourier-Hermite representation is exponentially more compact, thus yielding a classical algorithm that can match the performance of a previously proposed quantum algorithm for this problem. Further, this representation results in a system of linear ordinary differential equations (ODEs) which can be solved with well-developed quantum algorithms: a Hamiltonian simulation in the collisionless case, and quantum ODE solvers in the collisional case. In particular, we demonstrate that a quadratic speedup in system size is attainable.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗