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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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DOE OSTI2014

Robust estimation procedure in panel data model

The panel data modeling has received a great attention in econometric research recently. This is due to the availability of data sources and the interest to study cross sections of individuals observed over time. However, the problems may arise in modeling the panel in the presence of cross sectional dependence and outliers. Even though there are few methods that take into consideration the presence of cross sectional dependence in the panel, the methods may provide inconsistent parameter estimates and inferences when outliers occur in the panel. As such, an alternative method that is robust to outliers and cross sectional dependence is introduced in this paper. The properties and construction of the confidence interval for the parameter estimates are also considered in this paper. The robustness of the procedure is investigated and comparisons are made to the existing method via simulation studies. Our results have shown that robust approach is able to produce an accurate and reliable parameter estimates under the condition considered.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

How robust are the size measurements of high-redshift compact galaxies?

Massive quiescent galaxies at z ≈ 2 are apparently much more compact than galaxies of comparable mass today. How robust are these size measurements? We perform comprehensive simulations to determine possible biases and uncertainties in fitting single-component light distributions to real galaxies. In particular, we examine the robustness of the measurements of the luminosity, size, and other structural parameters. We devise simulations with increasing realism to systematically disentangle effects due to the technique (specifically using GALFIT) and the intrinsic structures of the galaxies. By accurately capturing the detailed substructures of nearby elliptical galaxies and then rescaling their sizes and signal-to-noise to mimic galaxies at different redshifts, we confirm that the massive quiescent galaxies at z ≈ 2 are significantly more compact intrinsically than their local counterparts. Their observed compactness is not a result of missing faint outer light due to systematic errors in modeling. In fact, we find that fitting multi-component galaxies with a single Sérsic profile, the procedure most commonly adopted in the literature, biases the inferred sizes higher by up to 10%-20%, which accentuates the amount of size evolution required. If the sky estimation has been done robustly and the model for the point-spread function is fairly accurate, GALFIT can retrieve the properties of single-component galaxies over a wide range of signal-to-noise ratios without introducing any systematic errors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Study of the element abundances in HD 140283: The abundance robustness of the weak r- and main r-process stars

Many works have attempted to investigate the astrophysical origin of the neutron-capture elements in the metal-poor star HD 140283. However, no definite conclusions have been drawn. In this work, using the abundance-decomposed approach, we find that the metal-poor star HD 140283 is a weak r-process star. Although this star is a weak r-process star, its Ba abundance mainly originates from the main r-process. This is the reason that the ratio [Ba/Eu]= −0.58±0.15 for HD 140283 is close to the ratio of the main r-process. Based on the comparison of the abundances in the six-weak r-process stars, we find that their element abundances possess a robust nature. On the other hand, we find that the robust nature of the abundance of the extreme main r-process stars ([r/Fe] ⩾ 1.5) can be extended to the lighter neutron-capture elements. Furthermore, the abundance characteristics of the weak r-process and main r-process are investigated. The abundance robustness of the two category r-process stars could be used as the constraint of the r-process theory and could be used to investigate the astrophysical origins of the elements in the metal-poor stars and population I stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Robustness of inflation to inhomogeneous initial conditions

We consider the effects of inhomogeneous initial conditions in both the scalar field profile and the extrinsic curvature on different inflationary models. In particular, we compare the robustness of small field inflation to that of large field inflation, using numerical simulations with Einstein gravity in 3+1 dimensions. We find that small field inflation can fail in the presence of subdominant gradient energies, suggesting that it is much less robust to inhomogeneities than large field inflation, which withstands dominant gradient energies. However, we also show that small field inflation can be successful even if some regions of spacetime start out in the region of the potential that does not support inflation. In the large field case, we confirm previous results that inflation is robust if the inflaton occupies the inflationary part of the potential. Furthermore, we show that increasing initial scalar gradients will not form sufficiently massive inflation-ending black holes if the initial hypersurface is approximately flat. Finally, we consider the large field case with a varying extrinsic curvature K , such that some regions are initially collapsing. We find that this may again lead to local black holes, but overall the spacetime remains inflationary if the spacetime is open, which confirms previous theoretical studies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Robustness of inflation to kinetic inhomogeneities

We investigate the effects of large inhomogeneities in both the inflaton field and its momentum. We find that in general, large kinetic perturbations reduce the number of e-folds of inflation. In particular, we observe that inflationary models with sub-Planckian characteristic scales are not robust even to kinetic energy densities that are sub-dominant to the potential energy density, unless the initial field configuration is sufficiently far from the minimum. This strengthens the results of our previous work. In inflationary models with super-Planckian characteristic scales, despite a reduction in the number of e-folds, inflation is robust even when the potential energy density is initially sub-dominant. For the cases we study, the robustness of inflation strongly depends on whether the inflaton field is driven into the reheating phase by the inhomogeneous scalar dynamics.

gravity↗
DOE OSTI2023

Multi-scale planning model for robust urban drought response

Increasingly severe droughts are straining municipal water resources and jeopardizing urban water security, but uncertainty in their duration, frequency, and intensity challenges drought planning and response. We develop the Drought Resilient Interscale Portfolio Planning model (DRIPP) to generate optimal planning responses to urban drought. DRIPP is a generalizable multi-scale framework for optimizing dynamic planning strategies of long-term infrastructure deployment and short-term drought response. It integrates climate and hydrological variability with high-fidelity representations of urban water distribution, available technology options, and demand reduction measures to yield robust and cost-effective water supply portfolios that are location-specific. We apply DRIPP in Santa Barbara, California to assess how least cost water supply portfolios vary under different drought scenarios and identify portfolios that are robust across drought scenarios. In Santa Barbara, we find that drought intensity, not duration or frequency, drives cost increases, reliability risk, and regret of overbuilding infrastructure. Under uncertain drought conditions, a diversified technology portfolio that includes both rapidly deployable, decentralized technologies alongside larger centralized technologies minimizes water supply cost while maintaining high robustness to climate uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation

Abstract Motivation Knowledge-guided learning offers effective and robust model training strategies in data-scarce settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and interpretability. By contrast, conventional deep learning approaches rely purely on data-driven learning, which can limit robust model interpretability, particularly in high-dimensional settings with limited size samples. In computational biology, knowledge-guided learning has primarily leveraged network- and structural-based knowledge, leading to biologically interpretable representations and enhanced predictive performance compared to conventional approaches. However, curated biomarkers, one of the most accessible forms of biological knowledge, remain largely unexplored within knowledge-guided paradigms. Results In this study, we propose a model-agnostic training paradigm, Biomarker-driven Explainable Prior-guided Learning (BioExPL), that can be applied to any neural networks that incorporates curated prior knowledge. BioExPL enforces neural networks to reflect curated biomarker priors in their latent representations through a novel knowledge-alignment loss. BioExPL consistently demonstrated significantly improved predictive performance and enhanced model interpretability with minimized computational overhead in simulation studies and intensive experiments on multiple cancer datasets. BioExPL not only integrates prior curated knowledge into the model but also accurately identifies unknown associated signals additionally. BioExPL is model-agnostic and domain-independent, enabling its integration into diverse neural network architectures. Availability and implementation The open-source is publicly available at: https://github.com/datax-lab/BioExPL.

Baek, Beomsu [Department of Computer Science, Univ↗
DOE OSTI2023

Angle-robust two-qubit gates in a linear ion crystal

In trapped-ion quantum computers, two-qubit entangling gates are generated by applying spin-dependent force which uses phonons to mediate interaction between the internal states of the ions. To maintain high-fidelity two-qubit gates under fluctuating experimental parameters, robust pulse-design methods are applied to remove the residual spin-motion entanglement in the presence of motional mode-frequency drifts. Here we propose an improved pulse-design method that also guarantees the robustness of the two-qubit rotation angle against uniform mode-frequency drifts by concatenating pulses with opposite sensitivity of the angle to mode-frequency drifts. Here we experimentally verify significantly improved robustness of the rotation angle against uniform mode-frequency drifts, as well as observe an improvement in gate fidelity from 97.84(10)% to 98.11(11)%, compared to a single frequency-modulated pulse.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Robust finite-temperature many-body scarring on a quantum computer

Mechanisms for suppressing thermalization in disorder-free many-body systems, such as Hilbert space fragmentation and quantum many-body scars, have recently attracted much interest in foundations of quantum statistical physics and potential quantum information processing applications. However, their sensitivity to realistic effects such as finite temperature remains largely unexplored. Here, we have utilized IBM's Kolkata quantum processor to demonstrate an unexpected robustness of quantum many-body scars at finite temperatures when the system is prepared in a thermal Gibbs ensemble. We identify such robustness in the PXP model, which describes quantum many-body scars in experimental systems of Rydberg atom arrays and ultracold atoms in tilted Bose-Hubbard optical lattices. By contrast, other theoretical models which host exact quantum many-body scars are found to lack such robustness and their scarring properties quickly decay with temperature. Our study sheds light on the important differences between scarred models in terms of their algebraic structures, which impacts their resilience to finite temperature. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Spin-orbit coupling induced robust spin-Seebeck effect and pure thermal spin currents in achiral molecule systems

Spin-Seebeck effect (SSE) is an effective route to realize pure spin current by using spin-polarized electrons or spin-wave transport in magnetic systems. In this work we propose a route, i.e., by using spin-orbit coupling (SOC), to achieve robust SSE characterized by pure thermal spin current. The material examples are constructed on achiral nanotubes, and the theoretical results reveal that (i) as temperature gradient is applied along the nanotubes, thermal spin-up and spin-down currents with opposite flow directions are produced without any accompanying charge current, (ii) the SSE is robust against decoherence and nonuniform interchain SOC, (iii) the thermal spin currents display a multioscillation feature with increasing device temperatures, supporting their potential device applications in thermal spin-current multiswitcher, and (vi) strain engineering in the radical direction of nanotubes is an effective way to improve SSE and to control pure thermal spin current. These inspiring spin transport behaviors in achiral molecular systems put forward a mechanism to realize the robust SSE characterized by pure spin current and develop the research field of spin-orbito-caloritronics, which focuses on the interplay of electrons’ spin and orbital degrees of freedom in the presence of temperature gradient.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Robust topological state against magnetic impurities observed in the superconductor PbTaSe 2

Magnetic impurities deposited on topological superconductor candidate PbTaSe 2 can introduce a nonsplitting zero-energy state inside the superconducting gap, which has been proposed as a field-free platform for topological zero modes. However, it is still unclear how robust the topological state in PbTaSe 2 is against magnetic impurities, which is related to the topological nature of the zero-energy state as well as its potential for quantum computation. In this paper, we use scanning tunneling microscopy to study the topological surface state in the normal state of PbTaSe 2 under the perturbation of magnetic impurities. We visualize the quasiparticle interference (QPI) arising from the topological surface state. We then deposit Fe impurities on the surface to form atomic Fe adatoms. We find that each Fe adatom sits at a unique interstitial position on the surface and features a local state at high energies, both of which are consistent with our first-principles calculation that further reveals its large magnetic moment. Our systematic Fe deposition and subsequent measurements show that the arclike QPI pattern at the Fermi energy is robust with up to 3% Fe coverage where the atomic nature of Fe adatoms still holds. Our results provide evidence that the topological surface state at the Fermi energy in PbTaSe 2 is robust against dilute magnetic impurities.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Robust analytic continuation of Green's functions via projection, pole estimation, and semidefinite relaxation

Green's functions of fermions are described by matrix-valued Herglotz-Nevanlinna functions. Since analytic continuation is fundamentally an ill-posed problem, the causal space described by the matrix-valued Herglotz-Nevanlinna structure can be instrumental in improving the accuracy and in enhancing the robustness with respect to noise. We demonstrate a three-pronged procedure for robust analytic continuation called PES: (1) projection of data to the causal space; (2) estimation of pole locations; and (3) semidefinite relaxation within the causal space. We compare the performance of PES with the recently developed Nevanlinna and Carathéodory continuation methods and find that PES is more robust in the presence of noise and does not require the usage of extended precision arithmetics. We also demonstrate that a causal projection improves the performance of the Nevanlinna and Carathéodory methods. The PES method is generalized to bosonic response functions, for which the Nevanlinna and Carathéodory continuation methods have not yet been developed. Furthermore, it is particularly useful for studying spectra with sharp features, as they occur in the study of molecules and band structures in solids.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Robust extraction of the proton charge radius from electron-proton scattering data

Background: Extracting the proton charge radius from electron scattering data requires determining the slope of the charge form factor at Q 2 15 of zero. But as experimental data never reach that limit, numerous methods for making the extraction have been proposed, though often the functions are determined after seeing the data which can lead to confirmation bias. Purpose: To find functional forms that will allow for a robust extraction of the input radius for a wide variety of functional forms in order to have confidence in the extraction from upcoming low Q 2 experimental data such as the Jefferson Lab PRad experiment. Method: We create a general framework for inputting form-factor functions as well as various fitting functions. The input form factors are used to generate pseudo-data with fluctuations intended to mimic the binning and random uncertainty of a given set of real data. All combinations of input functions and fit functions can then be tested repeatedly against regenerated pseudo-data. Since the input radius is known, this allows us to find those functions that are robust for radius extractions in an objective fashion. Results: For the range and uncertainty of the PRad data, we find that a two-parameter rational function, a two-parameter continued fraction and the second order polynomial expansion of z can extract the input radius regardless of the input charge form factor function that is used. Conclusions: We have created an easily expandable framework to search for functional forms that allow for a robust extraction of the radius from a given binning and uncertainty of pseudo-data generated from a wide variety of trial functions. This method has enabled a successful search for the best functional forms to extract the radius from the upcoming PRad data, and can be used for other experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Robust neural network-enhanced estimation of local primordial non-Gaussianity

When applied to the nonlinear matter distribution of the universe, neural networks have been shown to be very statistically sensitive probes of cosmological parameters, such as the linear perturbation amplitude σ 8 . However, when used as a “black box,” neural networks are not robust to baryonic uncertainty. We propose a robust architecture for constraining primordial non-Gaussianity f NL , by training a neural network to locally estimate σ 8 , and correlating these local estimates with the large-scale density field. We apply our method to N-body simulations, and show that σ⁡(f NL ) is 3.5 times better than the constraint obtained from a standard halo-based approach. Finally, we show that our method has the same robustness property as large-scale halo bias: baryonic physics can change the normalization of the estimated f NL , but cannot change whether f NL is detected.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Community detection robustness of graph neural networks

Graph neural networks (GNNs) are increasingly widely used for community detection in attributed networks. They combine structural topology with node attributes through message passing and pooling. However, their robustness or lack thereof with respect to different perturbations and targeted attacks in conjunction with community detection tasks is not well understood. To shed light on latent mechanisms behind GNN sensitivity on community detection tasks, we conduct a systematic computational evaluation of six widely adopted GNN architectures graph convolutional network, graph attention network, graph sample and aggregate (GraphSAGE), differentiable pooling (DiffPool), minimum cut pooling (MinCUT), and deep modularity networks (DMoN). The analysis covers three perturbation categories: node attribute manipulations, edge topology distortions, and adversarial attacks. We use element-centric similarity as the evaluation metric on synthetic benchmarks and real-world citation networks. Our findings indicate that supervised GNNs tend to achieve higher baseline accuracy, while unsupervised methods, particularly DMoN, maintain stronger resilience under targeted and adversarial perturbations. Furthermore, robustness appears to be strongly influenced by community strength, with well-defined communities reducing performance loss. Across all models, node attribute perturbations associated with targeted edge deletions and shifts in attribute distributions tend to cause the largest degradation in community recovery. These findings highlight important trade-offs between accuracy and robustness in GNN-based community detection and offer insights into selecting architectures resilient to noise and adversarial attacks.

Goel, Jaidev [Virginia Polytechnic Inst. and State↗
DOE OSTI2020

A Hybrid Framework Combining Model-Based and Data-Driven Methods for Hierarchical Decentralized Robust Dynamic State Estimation

This paper combines model-based and data-driven methods to develop a hierarchical, decentralized, robust dynamic state estimator (DSE). A two-level hierarchy is proposed, where the lower level consists of robust, model-based, decentralized DSEs. The state estimates sent from the lower level are received at the upper level, where they are filtered by a robust data-driven DSE after a principled sparse selection. This selection allows us to shrink the dimension of the problem at the upper level and hence significantly speed up the computational time. The proposed hybrid framework does not depend on the centralized infrastructure of the control centers; thus it can be completely embedded into the wide-area measurement systems. This feature will ultimately facilitate the placement of hierarchical decentralized control schemes at the phasor data concentrator locations. Also, the network model is not necessary; thus, a topology processor is not required. Finally, there is no assumption on the dynamics of the electric loads. The proposed framework is tested on the 2,000-bus synthetic Texas system, and shown to be capable of reconstructing the dynamic states of the generators with high accuracy, and of forecasting in the advent of missing data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Robust Scheduling of Microgrids With Resiliency Constraints

This paper proposes a novel microgrid scheduling model considering the resiliency requirements. The resiliency of the microgrid is guaranteed by quickly adjusting the output of committed local resources to supply the local demands continuously when the supply of power from the main grid is interrupted. To capture the prevailing uncertainties in renewable generation and demand, a two-stage robust optimization model is formulated to minimize the total operating cost under the worst realization of the modeled uncertainties. The column and constraint generation (CCG) method is used to solve the problem in an iterative manner. The solution of the proposed robust scheduling model guarantees the resiliency of the microgrid by supporting a quick islanding without consumer interruption and ensures the robustness against uncertainties in renewable generation and demand.

Liu, Guodong↗
DOE OSTI2022

Robust Solution Approach for Bilevel Demand Response Game at Distribution Level

In this paper, a bilevel electricity pricing and demand response game between a distribution system operator (DSO) and load aggregators (LAs) is considered, and a robust decision model is proposed for the DSO to deal with the uncertainties from the wholesale market prices and demand consumptions of LAs. With the max-min objective at the upper level, the robust bilevel model is converted into a single level model by the Karush-Kuhn-Tucker (KKT) conditions and prime-dual transformation. Several groups of experiments have been conducted based on different preferences on uncertainty gaps and peak load reductions to show its effectiveness. After-the-fact scenario analysis has indicated that the robust solution is more beneficial in reducing the risk of inaccurate predictions as compared to the risk neutral strategy.

Chen, Yang↗