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Results for “algorithmic differentiation”

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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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Circular polarization analyzer with polarization tunable focusing of surface plasmon polaritons

A practical circular polarization analyzer (CPA) that can selectively focus surface plasmon polaritons (SPPs) at two separate locations, according to the helicity of the circularly polarized light, is designed and experimentally verified in the terahertz frequency range. The CPA consists of fishbone-slit units and is designed using the simulated annealing algorithm. By differentially detecting the intensities of the two SPPs focuses, the helicity of the incident circularly polarized light can be obtained and the CPA is less vulnerable to the noise of incident light. The proposed device may also have wide potential applications in chiral SPPs photonics and the analysis of chiral molecules in biology.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

THE ADJOINT METHOD APPLIED TO TIME-DISTANCE HELIOSEISMOLOGY

For a given misfit function, a specified optimality measure of a model, its gradient describes the manner in which one may alter properties of the system to march toward a stationary point. The adjoint method, arising from partial-differential-equation-constrained optimization, describes a means of extracting derivatives of a misfit function with respect to model parameters through finite computation. It relies on the accurate calculation of wavefields that are driven by two types of sources, namely, the average wave-excitation spectrum, resulting in the forward wavefield, and differences between predictions and observations, resulting in an adjoint wavefield. All sensitivity kernels relevant to a given measurement emerge directly from the evaluation of an interaction integral involving these wavefields. The technique facilitates computation of sensitivity kernels (Frechet derivatives) relative to three-dimensional heterogeneous background models, thereby paving the way for nonlinear iterative inversions. An algorithm to perform such inversions using as many observations as desired is discussed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

OPTIMIZATION OF FAST FISSION GAS RELEASE MODEL PARAMETERS USING MACHINE LEARNING ACCELERATED EVOLUTIONARY ALGORITHMS

Fission gas release models used in FRAPCON and FAST are based on equations that describe the physical transport phenomena that control release from the fuel pellet to the rod void volume of fission gas produced during irradiation. Although these models are physically based, many of the parameters that control the release such as the impact of burnup on diffusion and bubble formation and saturation are critical to the accurate prediction of fission gas release, but cannot be measured in any quantitative way. To overcome this problem, these parameters have been empirically derived to provide a best fit to the available data that includes rod puncture data and electron probe microanalysis (EMPA) and X-ray fluorescence (XRF) data of radial location of residual gas within the pellet. In general, a heuristic approach for deriving these parameters via practical implementation of expert analyses on just a few dependent variables has been shown to perform quite well in most situations. In this paper, we present an approach for applying machine learning to expedite model development. This approach is based on developing a deep artificial neural network which describes FRAPCON’s fission gas release models, and optimizes parameters by a differential evaluation algorithm. This approach allows us to quickly and accurately tune physical models based on expert judgment, and works as a human-in-the-loop approach, to assist the modeler in identifying and addressing regions of high uncertainty in a multi-parameter space. Results of the updated fission gas release model will be shown for all the FAST assessment data.

Johns, Jesse M.↗
DOE OSTI2025

HFBTHO-AD: Differentiation of a nuclear energy density functional code

The HFBTHO code implements a nuclear energy density functional solver to model the structure of atomic nuclei. HFBTHO has previously been used to calibrate energy functionals and perform sensitivity analysis by using derivative-free methods. To enable derivative-based optimization and uncertainty quantification approaches, we must compute the derivatives of HFBTHO outputs with respect to the parameters of the energy functional, which are a subset of all input parameters of the code. Here, we use the algorithmic/automatic differentiation (AD) tool Tapenade to differentiate HFBTHO. We compare the derivatives obtained using AD against finite-difference approximation and examine the performance of the derivative computation.

Algorithmic differentiation↗
DOE OSTI2022

Q-POP-Thermo: A general-purpose thermodynamics solver for ferroelectric materials

We report that Q-POP-Thermo is a program designed to compute thermodynamic monodomain equilibrium states and their properties for ferroelectric single crystals and thin films based on the Landau-Ginzburg-Devonshire (LGD) Theory. Utilizing symbolic manipulation with the SymPy Library, the governing equations along with appropriate boundary conditions are solved for speedy minimization of the free energy of a crystal. Utilizing the popular Differential Evolution algorithm, with appropriate hybridization, multiple phase diagrams, such as the pressure-temperature phase diagram for bulk single crystals and the common strain-temperature phase diagram for monodomain thin-film systems can be readily generated. Furthermore, a variety of material properties of stable ferroelectric phases, including dielectric, piezoelectric, and electrocaloric properties, can simultaneously be calculated. Validation studies are presented for both thin-film and single crystal systems to test the effectiveness and capability of the open-source program.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Privacy-Preserving Knowledge Transfer with Bootstrap Aggregation of Teacher Ensembles

There is a need to transfer knowledge among institutions and organizations to save effort in annotation and labeling or in enhancing task performance. However, knowledge transfer is difficult because of restrictions that are in place to ensure data security and privacy. Institutions are not allowed to exchange data or perform any activity that may expose personal information. With the leverage of a differential privacy algorithm in a high-performance computing environment, we propose a new training protocol, Bootstrap Aggregation of Teacher Ensembles (BATE), which is applicable to various types of machine learning models. The BATE algorithm is based on and provides enhancements to the PATE algorithm, maintaining competitive task performance scores on complex datasets with underrepresented class labels.We conducted a proof-of-the-concept study of the information extraction from cancer pathology report data from four cancer registries and performed comparisons between four scenarios: no collaboration, no privacy-preserving collaboration, the PATE algorithm, and the proposed BATE algorithm. The results showed that the BATE algorithm maintained competitive macro-averaged F1 scores, demonstrating that the suggested algorithm is an effective yet privacy-preserving method for machine learning and deep learning solutions.

Yoon, Hong-Jun↗
DOE OSTI2019

Performance investigation of a high-field active magnetic regenerator

Regenerative magnetic cycles are of interest for small-scale, high-efficiency cryogen liquefiers; however, commercially relevant performance has yet to be demonstrated. To develop improved engineering prototypes, an efficient modeling tool is required to screen the multi-parameter design space. In this work, we describe an Active Magnetic Regenerative Refrigerator (AMRR) prototype using a high-field superconducting magnet that produces a 100 K temperature span. Using the experimental data, a semi-analytic AMR element model is validated and enhanced system performance is simulated using liquid propane as a heat transfer fluid. In addition, the regenerator composition and fluid flow are simultaneously optimized using a differential evolution algorithm. Simulation results indicate that a natural gas liquefier with a 160 K span and a second-law efficiency exceeding 20% is achievable.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2011

Gaussian beam decomposition of high frequency wave fields using expectation-maximization

A new numerical method for approximating highly oscillatory wave fields as a superposition of Gaussian beams is presented. The method estimates the number of beams and their parameters automatically. This is achieved by an expectation-maximization algorithm that fits real, positive Gaussians to the energy of the highly oscillatory wave fields and its Fourier transform. Beam parameters are further refined by an optimization procedure that minimizes the difference between the Gaussian beam superposition and the highly oscillatory wave field in the energy norm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Residual Monte Carlo high-order solver for Moment-Based Accelerated Thermal Radiative Transfer equations

In this article we explore the possibility of replacing Standard Monte Carlo (SMC) transport sweeps within a Moment-Based Accelerated Thermal Radiative Transfer (TRT) algorithm with a Residual Monte Carlo (RMC) formulation. Previous Moment-Based Accelerated TRT implementations have encountered trouble when stochastic noise from SMC transport sweeps accumulates over several iterations and pollutes the low-order system. With RMC we hope to significantly lower the build-up of statistical error at a much lower cost. First, we display encouraging results for a zero-dimensional test problem. Then, we demonstrate that we can achieve a lower degree of error in two one-dimensional test problems by employing an RMC transport sweep with multiple orders of magnitude fewer particles per sweep. We find that by reformulating the high-order problem, we can compute more accurate solutions at a fraction of the cost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

An ion specific continuum model on ionic surfactant's binary phase diagram in aqueous solution

Here, a continuum aggregation model is proposed to account for specific ion effects, enabling accurate phase diagrams calculations for the micellar, cylindrical and lamellar aggregates of sodium/potassium carboxylate surfactants in aqueous solution across a range of temperatures. Three groups of concentrations at distinctive temperatures are fitted to build empirical temperature dependence given the limited availability of relevant experimental measurements. The specific ion effects are manifested in the aggregates' surface tension as well as in the distributions of counter-ions' concentrations in the vicinity of the aggregates. The aggregates' geometric sizes are well-reproduced. The differential evolution algorithm is applied to address boundary conditions of the electrostatic potential, aggregate size optimization as well as the equilibrium of monomers transferring between the aggregate and aqueous region.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

Multi-bump solutions for the nonlinear Schroedinger-Poisson system

In this paper, we study a kind of nonlinear Schroedinger-Poisson system with a parameter {epsilon}. For any positive integer m, we prove that there exists {epsilon}(m) > 0 such that, for 0 < {epsilon} < {epsilon}(m), the equation has an m-bump positive solution under some suitable conditions. As a consequence, the equation has more and more multi-bump positive solutions as {epsilon}{yields} 0.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Nonlinear dynamical system approaches towards neural prosthesis

An asynchronous discrete-state spiking neurons is a wired system of shift registers that can mimic nonlinear dynamics of an ODE-based neuron model. The control parameter of the neuron is the wiring pattern among the registers and thus they are suitable for on-chip learning. In this paper an asynchronous discrete-state spiking neuron is introduced and its typical nonlinear phenomena are demonstrated. Also, a learning algorithm for a set of neurons is presented and it is demonstrated that the algorithm enables the set of neurons to reconstruct nonlinear dynamics of another set of neurons with unknown parameter values. The learning function is validated by FPGA experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

Periodically multilayered planar optical concentrator for photovoltaic solar cells

A planar optical concentrator comprising a periodic multilayered isotropic dielectric material backed by a metallic surface-relief grating was theoretically examined for silicon photovoltaics. The concentrator was optimized using a differential evolution algorithm for solar-spectrum-integrated power-flux density. Further optimization was carried out for tolerance to variations in the incidence angle, spatial dimensions, and dielectric properties. The average electron-hole pair density in a silicon solar cell can be doubled, and the material costs substantially diminished by this concentrator, whose efficacy is due to the excitation of waveguide modes and multiple surface-plasmon-polariton waves in a broad spectral regime.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Mathematical model of the metal mould surface temperature optimization

The article is focused on the problem of generating a uniform temperature field on the inner surface of shell metal moulds. Such moulds are used e.g. in the automotive industry for artificial leather production. To produce artificial leather with uniform surface structure and colour shade the temperature on the inner surface of the mould has to be as homogeneous as possible. The heating of the mould is realized by infrared heaters located above the outer mould surface. The conceived mathematical model allows us to optimize the locations of infrared heaters over the mould, so that approximately uniform heat radiation intensity is generated. A version of differential evolution algorithm programmed in Matlab development environment was created by the authors for the optimization process. For temperate calculations software system ANSYS was used. A practical example of optimization of heaters locations and calculation of the temperature of the mould is included at the end of the article.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Machine learning without a processor: Emergent learning in a nonlinear analog network

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic contrastive local learning networks (CLLNs) offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ significantly from artificial neural networks as well as the brain, so the feasibility and utility of incorporating nonlinear elements have not been explored. Here, we introduce a nonlinear CLLN—an analog electronic network made of self-adjusting nonlinear resistive elements based on transistors. We demonstrate that the system learns tasks unachievable in linear systems, including XOR (exclusive or) and nonlinear regression, without a computer. We find our decentralized system reduces modes of training error in order (mean, slope, curvature), similar to spectral bias in artificial neural networks. The circuitry is robust to damage, retrainable in seconds, and performs learned tasks in microseconds while dissipating only picojoules of energy across each transistor. This suggests enormous potential for fast, low-power computing in edge systems like sensors, robotic controllers, and medical devices, as well as manufacturability at scale for performing and studying emergent learning.

Science & Technology - Other Topics↗
DOE OSTI2020

Thermoeconomic cost optimization of superconducting magnets for proton therapy gantries

A compact gantry delivering 70-220 MeV protons with fixed field in the superconducting magnets could reduce the cost and improve the adoption of proton therapy. While a number of magnet and cryogenics designs have been proposed, the combined capital and operating costs of state-of-the-art superconducting materials have not been analyzed. In response, we develop a thermoeconomic model of a multi-stage, conduction cooled gantry lattice and analyze the cryocooler operating cost, cryocooler capital cost and conductor capital cost for Nb-Ti, Nb 3 Sn, REBCO and Bi-2223 over a continuous range of magnet temperatures, and a differential evolution algorithm is used to identify the optimal combination of thermal intercept temperatures. Although Nb3Sn yields the lowest Net Present Value (NPV) of $111.7k at a magnet temperature of 9.4 K, the optimized Bi-2223 design at 12.8 K approaches the realm of commercial feasibility by offering improved thermal stability and forgoing the need for costly conductor heat treatment and magnet quench training. Furthermore, it was found that Nb3Sn was more cost effective than Nb-Ti and that REBCO was not economically viable for the parameters of this investigation. Overall, the thermoeconomic model developed herein can optimize conductor choices, magnet temperatures and thermal staging which has value for any conduction-cooled superconducting magnet.

BSCCO↗
DOE OSTI2017

Accurate force field for molybdenum by machine learning large materials data

In this work, we present a highly accurate spectral neighbor analysis potential (SNAP) model for molybdenum (Mo) developed through the rigorous application of machine learning techniques on large materials data sets. Despite Mo's importance as a structural metal, existing force fields for Mo based on the embedded atom and modified embedded atom methods still do not provide satisfactory accuracy on many properties. We will show that by fitting to the energies, forces and stress tensors of a large density functional theory (DFT)-computed dataset on a diverse set of Mo structures, a Mo SNAP model can be developed that achieves close to DFT accuracy in the prediction of a broad range of properties, including energies, forces, stresses, elastic constants, melting point, phonon spectra, surface energies, grain boundary energies, etc. We will outline a systematic model development process, which includes a rigorous approach to structural selection based on principal component analysis, as well as a differential evolution algorithm for optimizing the hyperparameters in the model fitting so that both the model error and the property prediction error can be simultaneously lowered. We expect that this newly developed Mo SNAP model will find broad applications in large-scale, long-time scale simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2015

SU-C-BRD-03: Closing the Loop On Virtual IMRT QA

Purpose: To develop an algorithm that predicts a priori IMRT QA passing rates. Methods: 416 IMRT plans from all treatment sites were planned in Eclipse version 11 and delivered using a dynamic sliding window technique on Clinac iX or TrueBeam linacs (Varian Medical Systems, Palo Alto, CA). The 3%/3mm and 2%/2mm local distance to agreement (DTA) were recorded during clinical operations using a commercial 2D diode array (MapCHECK 2, Sun Nuclear, Melbourne, FL). Each plan was characterized by 37 metrics that describe different failure modes between the calculated and measured dose. Machine-learning algorithms (MLAs) were trained to learn the relation between the plan characteristics and each passing rate. Minimization of the cross validated error, together with maximum a posteriori estimation (MAP), were used to choose the model parameters. Results: 3%/3mm local DTA can be predicted with an error smaller than 3% for 98% of the plans. For the remaining 2% of plans, the residual error was within 5%. For 2%/2mm local DTA passing rates, 96% percent of the plans were successfully predicted with an error smaller than 5%. All high-risk plans that failed the 2%/2mm local criteria were correctly identified by the algorithm. The most important metric to describe the passing rates was determined to be the MU per Gray (modulation factor). Conclusions: Logs files and independent dose calculations have been suggested as possible substitutes for measurement based IMRT QA. However, none of these methods answer the fundamental question of whether a plan can be delivered with a clinically acceptable error given the limitations of the linacs and the treatment planning system. Predicting the IMRT QA passing rates a priori closes that loop. For additional robustness, virtual IMRT QA can be combined with Linac QA and log file analysis to confirm appropriate delivery.

60 APPLIED LIFE SCIENCES↗