Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “functional regression”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Visual discrimination in the pigeon (Columba livia): effects of selective lesions of the nucleus rotundus

The nucleus rotundus is a large thalamic nucleus in birds and plays a critical role in many visual discrimination tasks. In order to test the hypothesis that there are functionally distinct subdivisions in the nucleus rotundus, effects of selective lesions of the nucleus were studied in pigeons. The birds were trained to discriminate between different types of stationary objects and between different directions of moving objects. Multiple regression analyses revealed that lesions in the anterior, but not posterior, division caused deficits in discrimination of small stationary stimuli. Lesions in neither the anterior nor posterior divisions predicted effects in discrimination of moving stimuli. These results are consistent with a prediction led from the hypothesis that the nucleus is composed of functional subdivisions.

NASA Discipline Neuroscience↗
DOE OSTI2021

Statistical learning for predicting density–matrix-based electron dynamics

In this study, we consider the problem of learning density-dependent molecular Hamiltonian matrices from time series of electron density matrices, all in the context of Hartree–Fock theory. Prior work developed a solution to this problem for small molecular systems with density and Hamiltonian matrices of size at most 6 × 6. Here, using a battery of techniques, we scale prior methods to larger molecular systems with, for example, 29 × 29 matrices. This includes systems that either have more electrons or are expressed in large basis sets such as 6-311++G**. Scaling the method to larger systems enhances its relevance for realistic applications in chemistry and physics. To achieve this scaling, we apply dimensionality reduction, ridge regression and analytic computation of Hessians. Through the combination of these techniques, we are able to learn Hamiltonians by minimizing an objective function that encodes local propagation error. Importantly, these learned Hamiltonians can then be used to predict electron dynamics for thousands of steps: When we use our learned Hamiltonians to numerically solve the time-dependent Hartree–Fock equation, we obtain predicted dynamics that are in close quantitative agreement with ground truth dynamics. This includes field-off trajectories similar to the training data and field-on trajectories outside of the training data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Machine learning to accelerate screening for Marcus reorganization energies

Understanding and predicting the charge transport properties of π-conjugated materials is an important challenge for designing new organic electronic devices, such as solar cells, plastic transistors, light-emitting devices, and chemical sensors. A key component of the hopping mechanism of charge transfer in these materials is the Marcus reorganization energy which serves as an activation barrier to hole or electron transfer. While modern density functional methods have proven to accurately predict trends in intramolecular reorganization energy, such calculations are computationally expensive. In this work, we outline active machine learning methods to predict computed intramolecular reorganization energies of a wide range of polythiophenes and their use toward screening new compounds with low internal reorganization energies. Our models have an overall root mean square error (RMSE) of ±0.113 eV, but a much smaller RMSE of only ±0.036 eV on the new screening set. Since the larger error derives from high-reorganization energy compounds, the new method is highly effective to screen for compounds with potentially efficient charge transport parameters.

14 SOLAR ENERGY↗
DOE OSTI2013

Structural basis for angiopoietin-1–mediated signaling initiation

Angiogenesis is a complex cellular process involving multiple regulatory growth factors and growth factor receptors. Among them, the ligands for the endothelial-specific tunica intima endothelial receptor tyrosine kinase 2 (Tie2) receptor kinase, angiopoietin-1 (Ang1) and Ang2, play essential roles in balancing vessel stability and regression during both developmental and tumor-induced angiogenesis. Despite possessing a high degree of sequence identity, Ang1 and Ang2 have distinct functional roles and cell-signaling characteristics. Here, we present the crystal structures of Ang1 both unbound and in complex with the Tie2 ectodomain. Comparison of the Ang1-containing structures with their Ang2-containing counterparts provide insight into the mechanism of receptor activation and reveal molecular surfaces important for interactions with Tie2 coreceptors and associated signaling proteins. Using structure-based mutagenesis, we identify a loop within the angiopoietin P domain, adjacent to the receptor-binding interface, which confers the specific agonist/antagonist properties of the molecule. We demonstrate using cell-based assays that an Ang2 chimera containing the Ang1 loop sequence behaves functionally similarly to Ang1 as a constitutive Tie2 agonist, able to efficiently dissociate the inhibitory Tie1/Tie2 complex and elicit Tie2 clustering and downstream signaling.

cellular signaling↗
DOE OSTI2023

Tuning the magnetic properties of the CrMnFeCoNi Cantor alloy

Magnetic properties of more than 20 Cantor alloy samples of varying composition were investigated over a temperature range of 5 K to 300 K and in fields of up to 70 kOe using magnetometry and muon spin relaxation. Two transitions are identified: a spin-glass-like transition that appears between 55K and 190K, depending on composition, and a ferrimagnetic transition that occurs at approximately 43K in multiple samples with widely varying compositions. The magnetic signatures at 43K are remarkably insensitive to chemical composition. A modified Curie-Weiss model was used to fit the susceptibility data and to extract the net effective magnetic moment for each sample. The resulting values for the net effective moment were either diminished with increasing Cr or Mn concentrations or enhanced with decreasing Fe, Co, or Ni concentrations. Beyond a sufficiently large effective moment, the magnetic ground state transitions from ferrimagnetism to ferromagnetism. The effective magnetic moments, together with the corresponding compositions, are used in a global linear regression analysis to extract element-specific effective magnetic moments, which are compared to the values obtained by ab initio based density functional theory calculations. Finally, these moments provide the information necessary to controllably tune the magnetic properties of Cantor alloy variants.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Time-reversal symmetry breaking in two-dimensional nonequilibrium viscous fluids

In this work, we study the rheological signatures of departure from equilibrium in two-dimensional viscous fluids with and without internal spin. Under the assumption of isotropy, we provide the most general linear constitutive relations for stress and couple stress in terms of the velocity and spin fields. Invoking Onsager's regression hypothesis for fluctuations about steady states, we derive the Green-Kubo formulas relating the transport coefficients to time-correlation functions of the fluctuating stress. In doing so, we show that one of the nonequilibrium transport coefficients, the odd viscosity, requires time-reversal symmetry breaking in the case of systems without internal spin. However, the Green-Kubo relations for systems with internal spin also show that there is a possibility for nonvanishing odd viscosity even when time-reversal symmetry is preserved. Furthermore, we find that breakdown of equipartition in nonequilibrium steady states results in the decoupling of the two rotational viscosities relating the vorticity and the internal spin.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Non-intrusive nonlinear model reduction via machine learning approximations to low-dimensional operators

Abstract Although projection-based reduced-order models (ROMs) for parameterized nonlinear dynamical systems have demonstrated exciting results across a range of applications, their broad adoption has been limited by their intrusivity: implementing such a reduced-order model typically requires significant modifications to the underlying simulation code. To address this, we propose a method that enables traditionally intrusive reduced-order models to be accurately approximated in a non-intrusive manner. Specifically, the approach approximates the low-dimensional operators associated with projection-based reduced-order models (ROMs) using modern machine-learning regression techniques. The only requirement of the simulation code is the ability to export the velocity given the state and parameters; this functionality is used to train the approximated low-dimensional operators. In addition to enabling nonintrusivity, we demonstrate that the approach also leads to very low computational complexity, achieving up to $$10^3{\times }$$ 10 3 × in run time. We demonstrate the effectiveness of the proposed technique on two types of PDEs. The domain of applications include both parabolic and hyperbolic PDEs, regardless of the dimension of full-order models (FOMs).

42 ENGINEERING↗
DOE OSTI2022

Sivers extraction with Neural Network

Pseudo-data with simulated experimental errors can be generated to train an ensemble of Artificial Neural Networks (ANN) implemented on a regression to extract Transverse Momentum-dependent Distributions (TMDs). A preliminary analysis is presented on the reliability in extraction of the Sivers function imposed in the pseudo-data given the bounds on the experimental errors, data sparsity, and complexity of phase-space.

Fernando, Ishara↗
NASA NTRS1975

Methods Studies on System Identification from Transient Rotor Tests

Some of the more important methods are discussed that have been used or proposed for aircraft parameter identification. The methods are classified into two groups: Equation error or regression estimates and Bayesian estimates and their derivatives that are based on probabilistic concepts. In both of these two groups the cost function can be optimized either globally over the entire time span of the transient, or sequentially, leading to the formulation of optimum filters. Identifiability problems and the validation of the estimates are briefly outlined, and applications to lifting rotors are discussed.

Hohenemser, K. H.↗
DOE OSTI2025

High-Temperature Gas Sensor Materials with Properties Predicted via First-Principles Calculations with Machine Learning Modeling and Experimental Corroboration

Understanding the temperature dependence of functional properties of sensing materials is vital for their applications in combustion environments. The electron-phonon coupling that derives the electronic structure change with temperatures is a key property of interest as it affects other sensing responses. Herein, we first assess the temperature dependence of band gap renormalization in sensing materials by employing Allen-Heine-Cardona (AHC) theory with density functional theory (DFT) simulations corroborated with experimental observation. As the AHC calculations are impractical for high-throughput screening of materials, we employ data-driven Gaussian process regression to predict the parameters employed in the O’Donnell empirical model from a set of physical features. To mitigate the reliability issues arising from the small size of the dataset, we apply a Bayesian technique to improve the generalizability of the data-driven models as well as to quantify the uncertainty associated with theoretical predictions. These models capture well the overall trend of the O’Donnell parameters with respect to a reduced feature set obtained by transforming the available physical features. Quantifying the associated uncertainty helps us understand the reliability of the predictions and, therefore, the variation of bandgap as a function of temperature for other novel materials. The predicted candidates from machine learning models are further validated by experiments and DFT calculations.

bandgap renormalization↗
DOE OSTI2026

MOSAIC-CONUS: A Multimodal, Multi-Temporally Paired Dataset for Earth Sciences

Earth embeddings—vector representations of geographic locations indexed in space and time—are emerging as a unifying interface for geospatial AI. However, their quality depends not only on model design, but on how multimodal Earth observation (EO) data are spatially indexed, temporally aligned, and cross-modally associated during pretraining. We introduce MOSAIC-CONUS (Multimodal Observations with Spatially Aligned Imagery, Urban Points of Interest, In-Situ Measurements and Text Captions), a large-scale EO dataset over the contiguous United States, organized around 250,000 stratified point indices that serve as stable spatial keys across seven modalities: active radar, passive optical imagery, lidar-derived elevation, land cover, functional context, hydrometeorological measurements, and textual summaries. Unlike existing EO datasets, MOSAIC-CONUS introduces four contributions not jointly addressed in prior work: 1. an open-source, large-scale multimodal EO corpus structured around point-indexed data designed to support Earth embedding learning; 2. explicit radar-optical pairing tables spanning twelve temporal alignment regimes, formalizing cross-sensor alignment as a controllable variable for analyzing how temporal mismatch across modalities influences learned embeddings quality; 3. a benchmark suite spanning cross-modal retrieval, annual nightlights regression, and basin-held-out streamflow prediction, positioning MOSAIC-CONUS as a benchmark-ready resource for multimodal AI systems; and 4. a language-based embedding layer through co-registered textual summaries, enabling Earth embeddings to function as a queryable interface for agentic AI systems. The dataset and pairing protocols are publicly released.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Multiobjective Constrained Symbolic Regression for Predictive Modeling of Material Creep Behavior

When creep testing is repeated on samples of the same alloy under the same parametric conditions (i.e., stress and temperature), the resulting strain/time curves can vary from each other considerably as shown in Figure 1 [1]. The time required to creep test a material to rupture can extend to the order of years. Because of this, a numerical model that can quickly analyze the incomplete results of an ongoing experiment to predict 1) the incomplete portion of the strain/time curve leading up to the rupture point and 2) the rupture point itself would be of great utility to the materials community. Such a model has the potential to save 1) the time required to finish running the experiment to rupture 2) the associated monetary cost of finishing said experiment. Furthermore, it would be advantageous if the predictive model could give a parametric function modeling strain/time curves for material scientists to investigate the impact of the temperature and stress parameters on the resulting creep behavior. This work introduces a piecewise symbolic regression algorithm to predict the remainder of the strain/time curve. Preliminary results show good model performance.

36 MATERIALS SCIENCE↗
NASA NTRS2003

An evaluation of the use of new Doppler methods for detecting longitudinal function abnormalities in a pacing-induced heart failure model

BACKGROUND: Doppler tissue echocardiography and color M-mode Doppler flow propagation velocity have proven useful in evaluating cross-sections of patients with left ventricular (LV) dysfunction, but experience with serial changes is limited. Purpose and methods: We tested their use by evaluating the temporal changes of LV function in a pacing-induced congestive heart failure model. Rapid ventricular pacing was initiated and maintained in 20 dogs for 4 weeks. Echocardiography was performed at baseline and weekly during brief pacing cessation. RESULTS: With rapid pacing, LV volume significantly increased and ejection fraction (57%-28%), stroke volume (37-18 mL), and mitral annulus systolic velocity (16.1-6.6 cm/s) by Doppler tissue echocardiography significantly decreased, with ejection fraction and mitral annulus systolic velocity closely correlated (r = 0.706, P <.0001). In contrast to the mitral inflow velocities, mitral annulus early diastolic velocity decreased steadily (12.3-7.3 cm/s) resulting in a dramatic decrease in mitral annulus early/late (1.22-0.57) diastolic velocity with no tendency toward pseudonormalization. The color M-mode Doppler flow propagation velocity also showed significant steady decrease (57-24 cm/s) throughout the pacing period. Multiple regression analysis chose mitral annulus systolic velocity (r = 0.895, P <.0001) and propagation velocity (r = 0.782, P <.0001) for the most important factor predicting LV systolic and diastolic function, respectively. CONCLUSIONS: Doppler tissue echocardiography and color M-mode Doppler flow could evaluate the serial deterioration in LV dysfunction throughout the pacing period. These were more useful in quantifying progressive LV dysfunction than conventional ehocardiographic techniques, and were probably relatively independent of preload. These techniques could be suitable for longitudinal evaluation in addition to the cross-sectional study.

Evaluation Studies↗
DOE OSTI2025

Uncertainty quantification and sensitivity analysis of a nuclear thermal propulsion reactor startup sequence

The research presented in this article describes progress in applying stochastic methods, uncertainty quantification, parametric studies, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated via the radiation transport code Griffin to simulate neutronics. Our goal is to develop a reduced-order (surrogate) model that can be rapidly sampled with perturbations to multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums (CDs), which is itself controlled by a hybrid proportional-integral-derivative (PID) controller actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time [Λ] and effective delayed neutron fraction [ β eff ] from a transient Griffin simulation executed via Griffin’s improved quasi-static solver to provide the kinetic parameters) as inputs to functions that control the CD rotation angle. By investigating numerous stochastic approaches, we developed a dual-purpose surrogate model of the NTP system, using polynomial regression in the Multiphysics Object-Oriented Simulation Environment (MOOSE) Stochastic Tools Module (STM). The trained model can be rapidly sampled while simultaneously perturbing various input parameters, such as coefficients on the PID control or temperature (directly affecting the neutron cross section). The surrogate model delivers accurate (within 5%) results at speeds orders of magnitude faster (minutes, not days of computational time) than the base model. Once the surrogate model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs as well as the effects of these inputs on the model output. For example, coefficients used in the PID control system may vary due to some type of physical interference, or uncertainty may exist in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters, and operational boundaries can be determined. The goal of this work is to support development of an advanced control system for operating CDs in a functioning NTP system. This work is a scoping study of the MOOSE STM.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2025

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data-driven algorithms efficiently navigate such complex design spaces by learning relationships between material structures and performance metrics to discover high-performance functional materials. Surrogate-based active learning, continually improving its surrogate model by iteratively including high-quality data points, has emerged as a cost-effective data-driven approach. Furthermore, it can be coupled with quantum computing to enhance optimization processes, especially when paired with a special form of surrogate model (i.e., quadratic unconstrained binary optimization), formulated by factorization machine (FM). However, current practices often overlook the variability in design space sizes when determining the initial data size for optimization. In this work, we investigate the optimal initial data sizes required for efficient convergence across various design space sizes. By employing averaged piecewise linear regression, we identify initiation points where convergence begins, highlighting the crucial role of employing adequate initial data in achieving efficient optimization. These results contribute to the efficient optimization of functional materials by ensuring faster convergence and reducing computational costs in FM-based active learning.

active learning↗
DOE OSTI2018

Speckle-type POZ protein suppresses hepatocellular carcinoma cell migration and invasion via ubiquitin-dependent proteolysis of SUMO1/sentrin specific peptidase 7

Highlights: • SPOP expression was significantly downregulated in HCC and was associated with tumor size, differentiation and metastasis. • Low SPOP expression was a risk factor related to the prognosis of HCC patients. • SPOP inhibited HCC metastasis through interacting with SENP7 and promoting its degradation. Hepatocellular carcinoma (HCC) is associated with high metastatic potential and high mortality. Accumulating evidence has demonstrated that speckle-type POZ protein (SPOP) is a key adaptor molecule of ubiquitination. However, the molecular mechanism of SPOP-mediated ubiquitination in HCC metastasis remains obscure. In the present study, our results indicated that SPOP expression was significantly downregulated in HCC and was associated with tumor size, differentiation and metastasis. Cox regression model showed that low SPOP expression was a risk factor related to the prognosis of HCC patients. Loss- and gain-of-function assays demonstrated that SPOP inhibited HCC cell migration and invasion in vitro. Mechanisitically, co-immunoprecipitation and ubiquitination assays revealed that SPOP recognized and bound SENP7 and promoted its degradation via ubiquitin-dependent proteolysis. Analysis of immunohistochemistry showed that vimentin expression was correlated negatively with SPOP and positively with SENP7. These results implied that increased degradation of SENP7 by overexpression of SPOP decreased vimentin levels, which in turn attenuated HCC cell metastasis. Moreover, in vivo assays showed that SPOP overexpression also significantly suppressed liver and lung metastases. In summary, SPOP inhibits HCC cell metastasis via ubiquitin-dependent SENP7 proteolysis and may thus serve as a new opinion for the prevention of HCC metastasis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Decoding the Effect of Anion Identity on the Solubility of N -(2-(2-Methoxyethoxy)ethyl)phenothiazine (MEEPT)

Variations in the solubility of redox-active organic molecules (ROM) of interest for nonaqueous redox flow batteries (RFB), especially as the ROM state-of-charge changes during charge-discharge cycling, present significant molecular design challenges. The situation is further complicated as ROM solubility can be regulated by the choice of electrolyte salt and solvent that together with the ROM comprise the catholyte or anolyte (redox electrolyte) formulation, presenting materials design challenges. The ROM N-(2-(2-methoxyethoxy)ethyl)phenothiazine (MEEPT) is a viscous liquid at room temperature and is miscible in several organic solvents, including acetonitrile and propylene carbonate. The MEEPT radical cation (MEEPT +· ) paired with tetrafluoroborate (BF 4 - ) in acetonitrile presents a 0.5 M solubility, a dramatic decrease when compared to the viscous liquid of neutral MEEPT. Here, in this study, we present a joint experimental, regression modeling, and molecular dynamics (MD) simulations investigation to explore MEEPT-X (where X represents the counteranion) salt solubility variability as a function of concentration and counteranion chemistry in acetonitrile. We find a strong dependence of the salt solubility on the counteranion and relate these findings to explicit intermolecular interactions between MEEPT +· and the counteranion in the electrolyte solution.

Perera, Anton S. [Univ. of Kentucky, Lexington, KY↗
DOE OSTI2015

Bifurcation characteristics and safe basin of MSMA microgripper subjected to stochastic excitation

A kind of magnetic shape memory alloy (MSMA) microgripper is proposed in this paper, and its nonlinear dynamic characteristics are studied when the stochastic perturbation is considered. Nonlinear differential items are introduced to explain the hysteretic phenomena of MSMA, and the constructive relationships among strain, stress, and magnetic field intensity are obtained by the partial least-square regression method. The nonlinear dynamic model of a MSMA microgripper subjected to in-plane stochastic excitation is developed. The stationary probability density function of the system’s response is obtained, the transition sets of the system are determined, and the conditions of stochastic bifurcation are obtained. The homoclinic and heteroclinic orbits of the system are given, and the boundary of the system’s safe basin is obtained by stochastic Melnikov integral method. The numerical and experimental results show that the system’s motion depends on its parameters, and stochastic Hopf bifurcation appears in the variation of the parameters; the area of the safe basin decreases with the increase of the stochastic excitation, and the boundary of the safe basin becomes fractal. The results of this paper are helpful for the application of MSMA microgripper in engineering fields.

36 MATERIALS SCIENCE↗