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Results for “Inverse Optimization”

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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Distribution of ion current density on a rotating spherical cap substrate during ion-assisted deposition

The uniformity of ion density is critical for applications relying on the ion assisted deposition technique for the fabrication of the high quality thin films. The authors propose and describe here a method allowing one to calculate the ion density distribution on spherical substrate holders under stationary and rotating conditions for different positions of the ion source. The ion beam shape was approximated by a cos{sup n} function, and the ion current density was represented by a function inversely proportional to the distance from the ion source in accordance with our experimental results. As an example, a calculation of the current density distribution on the spherical cap substrate was performed for a broad beam ion source operated with an anode current of 3 A. The authors propose an approach for process optimization with respect to the ion source position and its inclination, in terms of uniformity and absolute value of the ion current density.

42 ENGINEERING↗
DOE OSTI2022

Design-Space Exploration for Inverse-Design of Wind Turbine Blades Using Data-Driven Methods

The state-of-the-practice aerodynamic design methods for wind turbine blades is typically based on Blade-Element Momentum (BEM) theory using a pre-designed frozen family of airfoils. The airfoils are themselves typically designed using panel methods. The design of the next-generation of large flexible rotors will need to capture the non-linear aerodynamics and three-dimensional flow to reduce the levelized cost of wind energy. Data-driven methods for aerodynamic design using data generated by computational fluid dynamics offer an attractive alternative to BEM-based methods that captures the non-linear aerodynamics of the component airfoils as well as the root and tip sections. In this work, we develop and demonstrate a framework to "smartly" explore the relevant design space in combination with an appropriate automated CFD pipeline to evaluate the aerodynamics of each design. The design-space exploration framework uses appropriate perturbations to the airfoil shape and induction profile from a baseline shape in combination with the inverse-design using BEM. The resulting designs are evaluated using an automated CFD pipeline using the in-house CFD solver framework "Mercury". We perform verification and validation to establish the capability of the Mercury framework to predict the aerodynamic performance of wind turbines. The CFD simulation of the perturbed blade shapes are optimized to restart from the converged baseline simulation to reduce the computational time. Finally, we demonstrate the design-space exploration technique for the design of the outboard section and the full rotor using perturbations to the shape and operating conditions of the NREL 5-MW turbine.

aerodynamic design methods↗
DOE OSTI2019

Asymptotic Behavior of Memristive Circuits

The interest in memristors has risen due to their possible application both as memory units and as computational devices in combination with CMOS. This is in part due to their nonlinear dynamics, and a strong dependence on the circuit topology. We provide evidence that also purely memristive circuits can be employed for computational purposes. In the present paper we show that a polynomial Lyapunov function in the memory parameters exists for the case of DC controlled memristors. Such a Lyapunov function can be asymptotically approximated with binary variables, and mapped to quadratic combinatorial optimization problems. This also shows a direct parallel between memristive circuits and the Hopfield-Little model. In the case of Erdos-Renyi random circuits, we show numerically that the distribution of the matrix elements of the projectors can be roughly approximated with a Gaussian distribution, and that it scales with the inverse square root of the number of elements. This provides an approximated but direct connection with the physics of disordered system and, in particular, of mean field spin glasses. Using this and the fact that the interaction is controlled by a projector operator on the loop space of the circuit. We estimate the number of stationary points of the approximate Lyapunov function and provide a scaling formula as an upper bound in terms of the circuit topology only.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

New results concerning the use of kinematically redundant manipulators in microgravity environments

This paper is concerned with the development of control strategies for kinematically redundant manipulators to be used in Space Station laboratories. These robots must be able to conduct experiments and manufacturing processes without disturbing the microgravity environment, and thus their dynamic base reactions/motions must be nearly eliminated. Redundant degrees of freedom permit the inverse kinematics problem to be solved in conjuction with the minimization of a cost function defined as a weighted sum of the base reactions. It is shown that the weights in the cost function should be used as an additional set of parameters in the minimization problem. The actual performance of the local optimal trajectory approach is demonstrated to be not as good as was previously reported.

Quinn, R. D.↗
DOE OSTI2015

The IceCube Neutrino Observatory, the Pierre Auger Observatory and the Telescope Array: Joint Contribution to the 34th International Cosmic Ray Conference (ICRC 2015)

We have conducted three searches for correlations between ultra-high energy cosmic rays detected by the Telescope Array and the Pierre Auger Observatory, and high-energy neutrino candidate events from IceCube. Two cross-correlation analyses with UHECRs are done: one with 39 cascades from the IceCube `high-energy starting events' sample and the other with 16 high-energy `track events'. The angular separation between the arrival directions of neutrinos and UHECRs is scanned over. The same events are also used in a separate search using a maximum likelihood approach, after the neutrino arrival directions are stacked. To estimate the significance we assume UHECR magnetic deflections to be inversely proportional to their energy, with values $3^\circ$, $6^\circ$ and $9^\circ$ at 100 EeV to allow for the uncertainties on the magnetic field strength and UHECR charge. A similar analysis is performed on stacked UHECR arrival directions and the IceCube sample of through-going muon track events which were optimized for neutrino point-source searches.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking

In the past few years, Generative Adversarial Networks (GANs) have dramatically advanced our ability to represent and parameterize high-dimensional, non-linear image manifolds. As a result, they have been widely adopted across a variety of applications, ranging from challenging inverse problems like image completion, to problems such as anomaly detection and adversarial defense. A recurring theme in many of these applications is the notion of projecting an image observation onto the manifold that is inferred by the generator. In this context, Projected Gradient Descent (PGD) has been the most popular approach, which essentially optimizes for a latent vector that minimizes the discrepancy between a generated image and the given observation. However, PGD is a brittle optimization technique that fails to identify the right projection (or latent vector) when the observation is corrupted, or perturbed even by a small amount. Such corruptions are common in the real world, for example images in the wild come with unknown crops, rotations, missing pixels, or other kinds of non-linear distributional shifts which break current encoding methods, rendering downstream applications unusable. To address this, we propose corruption mimicking—a new robust projection technique, that utilizes a surrogate network to approximate the unknown corruption directly at test time, without the need for additional supervision or data augmentation. The proposed method is significantly more robust than PGD and other competing methods under a wide variety of corruptions, thereby enabling a more effective use of GANs in real-world applications. Finally, more importantly, we show that our approach produces state-of-the-art performance in several GAN-based applications—anomaly detection, domain adaptation, and adversarial defense, that benefit from an accurate projection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Inducing a tunable skyrmion-antiskyrmion system through ion beam modification of FeGe films

Abstract Skyrmions and antiskyrmions are nanoscale swirling textures of magnetic moments formed by chiral interactions between atomic spins in magnetic noncentrosymmetric materials and multilayer films with broken inversion symmetry. These quasiparticles are of interest for use as information carriers in next-generation, low-energy spintronic applications. To develop skyrmion-based memory and logic, we must understand skyrmion-defect interactions with two main goals—determining how skyrmions navigate intrinsic material defects and determining how to engineer disorder for optimal device operation. Here, we introduce a tunable means of creating a skyrmion-antiskyrmion system by engineering the disorder landscape in FeGe using ion irradiation. Specifically, we irradiate epitaxial B20-phase FeGe films with 2.8 MeV Au 4+ ions at varying fluences, inducing amorphous regions within the crystalline matrix. Using low-temperature electrical transport and magnetization measurements, we observe a strong topological Hall effect with a double-peak feature that serves as a signature of skyrmions and antiskyrmions. These results are a step towards the development of information storage devices that use skyrmions and antiskyrmions as storage bits, and our system may serve as a testbed for theoretically predicted phenomena in skyrmion-antiskyrmion crystals.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Three-dimensional particle-in-cell simulations of laser-driven multiradiation sources based on double-layer targets

Double-layer targets (DLTs), made of a low-density foam on top of a solid substrate, can efficiently convert the energy of a high-intensity laser to provide sources of photons and protons. We investigate a 30-fs pulse with a peak intensity of I ~ 8.7×10 20 W/cm 2 and a peak power of ~120 TW interacting with a DLT using three-dimensional (3D) particle-in-cell simulations. We focus on providing quantitative results in full 3D geometry on the foam thickness dependence; on the competition between two photon-generating processes in DLTs, i.e., nonlinear inverse Compton scattering (NICS) and bremsstrahlung (BS); and on the acceleration of protons via enhanced target-normal sheath acceleration. Here, we discuss conversion efficiency, average energy, and angular distributions of such multiradiation sources. We find that NICS can prevail over BS if the DLT's substrate is thin enough (~µ⁢m) and that the optimal foam thickness that maximizes the conversion efficiency in NICS and BS photons and the proton cutoff energy, among those considered, is the same (15µ⁢m). These results show that DLTs constitute an excellent tool for developing relatively compact and optimized laser-driven multicomponent radiation sources.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Multiobjective and categorical global optimization of photonic structures based on ResNet generative neural networks

Abstract We show that deep generative neural networks, based on global optimization networks (GLOnets), can be configured to perform the multiobjective and categorical global optimization of photonic devices. A residual network scheme enables GLOnets to evolve from a deep architecture, which is required to properly search the full design space early in the optimization process, to a shallow network that generates a narrow distribution of globally optimal devices. As a proof-of-concept demonstration, we adapt our method to design thin-film stacks consisting of multiple material types. Benchmarks with known globally optimized antireflection structures indicate that GLOnets can find the global optimum with orders of magnitude faster speeds compared to conventional algorithms. We also demonstrate the utility of our method in complex design tasks with its application to incandescent light filters. These results indicate that advanced concepts in deep learning can push the capabilities of inverse design algorithms for photonics.

Jiang, Jiaqi↗
NASA NTRS1977

Ambiguity resolution for satellite Doppler positioning systems

A test for ambiguity resolution was derived which was the most powerful in the sense that it maximized the probability of a correct decision. When systematic error sources were properly included in the least squares reduction process to yield an optimal solution, the test reduced to choosing the solution which provided the smaller valuation of the least squares loss function. When systematic error sources were ignored in the least squares reduction, the most powerful test was a quadratic form comparison with the weighting matrix of the quadratic form obtained by computing the pseudo-inverse of a reduced rank square matrix. A formula is presented for computing the power of the most powerful test. A numerical example is included in which the power of the test is computed for a situation which may occur during an actual satellite aided search and rescue mission.

Argentiero, P. D.↗
NASA NTRS1989

Online submicron particle sizing by dynamic light scattering using autodilution

Efficient production of a wide range of commercial products based on submicron colloidal dispersions would benefit from instrumentation for online particle sizing, permitting real time monitoring and control of the particle size distribution. Recent advances in the technology of dynamic light scattering (DLS), especially improvements in algorithms for inversion of the intensity autocorrelation function, have made it ideally suited to the measurement of simple particle size distributions in the difficult submicron region. Crucial to the success of an online DSL based instrument is a simple mechanism for automatically sampling and diluting the starting concentrated sample suspension, yielding a final concentration which is optimal for the light scattering measurement. A proprietary method and apparatus was developed for performing this function, designed to be used with a DLS based particle sizing instrument. A PC/AT computer is used as a smart controller for the valves in the sampler diluter, as well as an input-output communicator, video display and data storage device. Quantitative results are presented for a latex suspension and an oil-in-water emulsion.

Nicoli, David F.↗
NASA NTRS2007

Design of a Low-Energy FARAD Thruster

The design of an electrodeless thruster that relies on a pulsed, rf-assisted discharge and electromagnetic acceleration using an inductive coil is presented. The thruster design is optimized using known performance,scaling parameters, and experimentally-determined design rules, with design targets for discharge energy, plasma exhaust velocity; and thrust efficiency of 100 J/pulse, 25 km/s, and 50%, respectively. Propellant is injected using a high-speed gas valve and preionized by a pulsed-RF signal supplied by a vector inversion generator, allowing for current sheet formation at lower discharge voltages and energies relative to pulsed inductive accelerators that do not employ preionization. The acceleration coil is designed to possess an inductance of at least 700 nH while the target stray (non-coil) inductance in the circuit is 70 nH. A Bernardes and Merryman pulsed power train or a pulse compression power train provide current to the acceleration coil and solid-state components are used to switch both powertrains.

Polzin, K. A.↗
DOE OSTI2017

Estimating Jupiter’s Gravity Field Using Juno Measurements, Trajectory Estimation Analysis, and a Flow Model Optimization

The upcoming Juno spacecraft measurements have the potential of improving our knowledge of Jupiter’s gravity field. The analysis of the Juno Doppler data will provide a very accurate reconstruction of spatial gravity variations, but these measurements will be very accurate only over a limited latitudinal range. In order to deduce the full gravity field of Jupiter, additional information needs to be incorporated into the analysis, especially regarding the Jovian flow structure and its depth, which can influence the measured gravity field. In this study we propose a new iterative method for the estimation of the Jupiter gravity field, using a simulated Juno trajectory, a trajectory estimation model, and an adjoint-based inverse model for the flow dynamics. We test this method both for zonal harmonics only and with a full gravity field including tesseral harmonics. The results show that this method can fit some of the gravitational harmonics better to the “measured” harmonics, mainly because of the added information from the dynamical model, which includes the flow structure. Thus, it is suggested that the method presented here has the potential of improving the accuracy of the expected gravity harmonics estimated from the Juno and Cassini radio science experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Neural networks (NNs) are currently changing the computational paradigm on how to combine data with mathematical laws in physics and engineering in a profound way, tackling challenging inverse and ill-posed problems not solvable with traditional methods. However, quantifying errors and uncertainties in NN-based inference is more complicated than in traditional methods. This is because in addition to aleatoric uncertainty associated with noisy data, there is also uncertainty due to limited data, but also due to NN hyperparameters, overparametrization, optimization and sampling errors as well as model misspecification. Although there are some recent works on uncertainty quantification (UQ) in NNs, there is no systematic investigation of suitable methods towards quantifying the total uncertainty effectively and efficiently even for function approximation, and there is even less work on solving partial differential equations and learning operator mappings between infinite-dimensional function spaces using NNs. In this work, we present a comprehensive framework that includes uncertainty modeling, new and existing solution methods, as well as evaluation metrics and post-hoc improvement approaches. Further, to demonstrate the applicability and reliability of our framework, we present an extensive comparative study in which various methods are tested on prototype problems, including problems with mixed input-output data, and stochastic problems in high dimensions. In the Appendix, we include a comprehensive description of all the UQ methods employed. Further, to help facilitate the deployment of UQ in Scientific Machine Learning research and practice, we present and develop in [1] an open-source Python library (github.com/Crunch-UQ4MI/neuraluq), termed NeuralUQ, that is accompanied by an educational tutorial and additional computational experiments.

11 physics-informed neural networks↗
DOE OSTI2018

Evolution of Active Sites in Pt-Based Nanoalloy Catalysts for the Oxidation of Carbonaceous Species by Combined in Situ Infrared Spectroscopy and Total X-ray Scattering

Here, we present results from combined in situ infrared spectroscopy and total X-ray scattering studies on the evolution of catalytically active sites in exemplary binary and ternary Pt-based nanoalloys during a sequence of CO oxidation–reactivation–CO oxidation reactions. We find that when within a particular compositional range, the fresh nanoalloys may exhibit high catalytic activity for low-temperature CO oxidation. Using surface-specific atomic pair distribution functions (PDFs) extracted from the in situ total X-ray scattering data, we find that, regardless of their chemical composition and initial catalytic activity, the fresh nanoalloys suffer a significant surface structural disorder during CO oxidation. Upon reactivation in oxygen atmosphere, the surface of used nanoalloy catalysts both partially oxidizes and orders. Remarkably, it largely retains its structural state when the nanoalloys are reused as CO oxidation catalysts. The seemingly inverse structural changes of studied nanoalloy catalysts occurring under CO oxidation and reactivation conditions affect the active sites on their surface significantly. In particular, through different mechanisms, both appear to reduce the CO binding strength to the nanoalloy’s surface and thus increase the catalytic stability of the nanoalloys. The findings provide clues for further optimization of nanoalloy catalysts for the oxidation of carbonaceous species through optimizing their composition, activation, and reactivation. Besides, the findings demonstrate the usefulness of combined in situ infrared spectroscopy and total X-ray scattering coupled to surface-specific atomic PDF analysis to the ongoing effort to produce advanced catalysts for environmentally and technologically important applications.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Orbital-dependent second-order scaled-opposite-spin correlation functionals in the optimized effective potential method

The performance of correlated optimized effective potential (OEP) functionals based on the spin-resolved second-order correlation energy is analysed. The relative importance of singly- and doubly- excited contributions as well as the effect of scaling the same- and opposite- spin components is investigated in detail comparing OEP results with Kohn–Sham (KS) quantities determined via an inversion procedure using accurate ab initio electronic densities. Special attention is dedicated in particular to the recently proposed scaled-opposite–spin OEP functional [I. Grabowski, E. Fabiano, and F. Della Sala, Phys. Rev. B 87, 075103 (2013)] which is the most advantageous from a computational point of view. We find that for high accuracy, a careful, system dependent, selection of the scaling coefficient is required. We analyse several size-extensive approaches for this selection. Finally, we find that a composite approach, named OEP2-SOSh, based on a post-SCF rescaling of the correlation energy can yield high accuracy for many properties, being comparable with the most accurate OEP procedures previously reported in the literature but at substantially reduced computational effort.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

MTUQ: a framework for estimating moment tensors, point forces, and their uncertainties

SUMMARY We introduce MTUQ, an open-source Python package for seismic source estimation and uncertainty quantification, emphasizing flexibility and operational scalability. MTUQ provides MPI-parallelized grid search and global optimization capabilities, compatibility with 1-D and 3-D Green’s function database formats, customizable data processing, C-accelerated waveform and first-motion polarity misfit functions, and utilities for plotting seismic waveforms and visualizing misfit and likelihood surfaces. Applicability to a range of full- and constrained-moment tensor, point force, and centroid inversion problems is possible via a documented application programming interface, accompanied by example scripts and integration tests. We demonstrate the software using three different types of seismic events: (1) a 2009 intraslab earthquake near Anchorage, Alaska; (2) an episode of the 2021 Barry Arm landslide in Alaska; and (3) the 2017 Democratic People’s Republic of Korea underground nuclear test. With these events, we illustrate the well-known complementary character of body waves, surface waves, and polarities for constraining source parameters. We also convey the distinct misfit patterns that arise from each individual data type, the importance of uncertainty quantification for detecting multimodal or otherwise poorly constrained solutions, and the software’s flexible, modular design.

58 GEOSCIENCES↗
DOE OSTI2016

SU-F-T-346: Dose Mimicking Inverse Planning Based On Helical Delivery Treatment Plans for Head and Neck Patients

Purpose: We aim to evaluate a new commercial dose mimicking inverse-planning application that was designed to provide cross-platform treatment planning, for its dosimetric quality and efficiency. The clinical benefit of this application allows patients treated on O-shaped linac to receive an equivalent plan on conventional L-shaped linac as needed for workflow or machine downtime. Methods: The dose mimicking optimization process seeks to create a similar DVH of an O-shaped linac-based plans with an alternative treatment technique (IMRT or VMAT), by maintaining target conformity, and penalizing dose falloff outside the target. Ten head and neck (HN) helical delivery plans, including simple and complex cases were selected for re-planning with the dose mimicking application. All plans were generated for a 6 MV beam model, using 7-field/ 9-field IMRT and VMAT techniques. PTV coverage (D1, D99 and homogeneity index [HI]), and OARs avoidance (Dmean / Dmax) were compared. Results: The resulting dose mimicked HN plans achieved acceptable PTV coverage for HI (VMAT 7.0±2.3, 7-fld 7.3±2.4, and 9-fld 7.0±2.4), D99 (98.0%±0.7%, 97.8%±0.7%, and 98.0%±0.7%), as well as D1 (106.4%±2.1%, 106.5%±2.2%, and 106.4%±2.1%), respectively. The OAR dose discrepancy varied: brainstem (2% to 4%), cord (3% to 6%), esophagus (−4% to −8%), larynx (−4% to 2%), and parotid (4% to 14%). Mimicked plans would typically be needed for 1–5 fractions of a treatment course, and we estimate <1% variance would be introduced in target coverage while maintaining comparable low dose to OARs. All mimicked plans were approved by independent physician and passed patient specific QA within our established tolerance. Conclusion: Dose mimicked plans provide a practical alternative for responding to clinical workflow issues, and provide reliability for patient treatment. The quality of dose mimicking for HN patients highly depends on the delivery technique, field numbers and angles, as well as user selection of structures.

60 APPLIED LIFE SCIENCES↗