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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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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

The rate of mass loss and variations in the wind from the Be star delta Centauri

Copernicus ultraviolet scans of the Be star delta Centauri obtained in 1976 and 1979, show a significant variation in the Si III lambda 1206 profile, The strong asymmetry that was present in 1976 had disappeared by 1979. The Si IV lambda 1400 doublet was also asymmetric in 1976, but was not observed in 1979. A quantitative fit of the line shapes to theoretical wind profiles shows that the mass-loss rate in 1976 was 2 x 10 to the minus 8th power/yr, and that the rate of mass loss in Si III was at least one order of magnitude less in 1979. It is not possible to determine whether the variation represented an overall change in the lass-loss rate, or whether it was due to a change in the ionization balance. The profile fitting procedure resulted in the adoption of assumed underlying photospheric Si III and Si IV profiles, and the equivalent widths measured from these profiles are most consistent with T sub eff between 30,000 and 35,000 K, somewhat hotter than implied by the spectral classification normally assigned to this star. The ultraviolet photospheric line widths, coupled wit published theoretical analyses of rotational gravitational darkening, imply an intrinsic equatorial velocity of about 310 km/sec and an angle of inclination of the rotational axis to the line of sight of i is less than or equal to 44 deg.

Snow, T. P., Jr.↗
DOE OSTI2025

Assessing Dynamic Time Warping Techniques for Discriminating Seismic Sources at Local and Regional Distances

Effective monitoring of seismic explosions and hazard assessment relies heavily on the accurate discrimination of underground seismic sources. This study investigates the application of novel nonlinear alignment techniques, specifically Dynamic Time Warping (DTW), for event-type discrimination at regional and local distances. Building on prior research that used DTW and Elastic Shape Analysis (ESA) in discrimination at regional distances, we evaluate the performance of recently developed variants of DTW, including a method that employs Pearson cross-correlation as a measure of warping distance and a time distortion coefficient that quantifies the type and degree of time distortion between signals. By analyzing observational datasets that include different source types, we assess the performance of these approaches for realistic monitoring scenarios. Specifically, we consider a dataset recorded at regional distances in the Korean Peninsula and a local-distance subset from the Unconstrained Utah Event Bulletin catalog to evaluate DTW-based discrimination across multiple distance scales. Additionally, we introduce the maximum cross-correlations of warped waveforms as a similarity metric for event classification. Through hierarchical cluster analysis and dendrogram interpretation, we present our findings, highlighting the strengths and limitations of these techniques in seismic event classification.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS2013

An Overview of Modifications Applied to a Turbulence Response Analysis Method for Flexible Aircraft Configurations

A software program and associated methodology to study gust loading on aircraft exists for a classification of geometrically simplified flexible configurations. This program consists of a simple aircraft response model with two rigid and three flexible symmetric degrees of freedom and allows for the calculation of various airplane responses due to a discrete one-minus-cosine gust as well as continuous turbulence. Simplifications, assumptions, and opportunities for potential improvements pertaining to the existing software program are first identified, then a revised version of the original software tool is developed with improved methodology to include more complex geometries, additional excitation cases, and output data so as to provide a more useful and accurate tool for gust load analysis. Revisions are made in the categories of aircraft geometry, computation of aerodynamic forces and moments, and implementation of horizontal tail mode shapes. In order to improve the original software program to enhance usefulness, a wing control surface and horizontal tail control surface is added, an extended application of the discrete one-minus-cosine gust input is employed, a supplemental continuous turbulence spectrum is implemented, and a capability to animate the total vehicle deformation response to gust inputs in included. These revisions and enhancements are implemented and an analysis of the results is used to validate the modifications.

Funk, Christie J.↗
DOE OSTI2015

A new sample of obscured AGNs selected from the XMM-Newton and AKARI surveys

We report a new sample of obscured active galactic nuclei (AGNs) selected from the XMM-Newton serendipitous source and AKARI point-source catalogs. We match X-ray sources with infrared (18 and 90 μm) sources located at |b|>10{sup ∘} to create a sample consisting of 173 objects. Their optical classifications and absorption column densities measured by X-ray spectra are compiled and study efficient selection criteria to find obscured AGNs. We apply the criteria (1) X-ray hardness ratio defined by using the 2–4.5 keV and 4.5–12 keV bands > −0.1 and (2) EPIC-PN count rate (CR) in the 0.2–12 keV to infrared flux ratio CR/F{sub 90}<0.1 or CR/F{sub 18}<1, where F{sub 18} and F{sub 90} are infrared fluxes at 18 and 90 μm in Jy, respectively, to search for obscured AGNs. X-ray spectra of 48 candidates, for which no X-ray results have been published, are analyzed and X-ray evidence for the presence of obscured AGNs such as a convex shape X-ray spectrum indicative of absorption of N{sub H} ∼ 10{sup 22–24} cm{sup −2}, a very flat continuum, or a strong Fe–K emission line with an equivalent width of >700 eV is found in 26 objects. Six of them are classified as Compton-thick AGNs, and four are represented by either Compton-thin or Compton-thick spectral models. The success rate of finding obscured AGNs combining our analysis and the literature is 92% if the 18 μm condition is used. Of the 26 objects, 4 are optically classified as an H ii nucleus and are new “elusive AGNs” in which star formation activity likely overwhelms AGN emission in the optical and infrared bands.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRSDate not supplied

CONUS and Global Carbon Consumption Database for Wildland Fire

Fire plays a significant role on both national and global scales, profoundly impacting landscapes shaped by human activity as well as wildlands. Even though fire can be devastating, wildland fire is a natural and integral force on our landscapes. Fires can also serve to reduce fuels to mitigate wildfire risk and maintain healthy ecosystem functions. However, the smoke produced by fires, regardless of their size or purpose, can pose adverse effects on human health when inhaled downwind. Understanding the influence of smoke on air quality and human well-being necessitates the quantification of emissions that fires release into the atmosphere. In response to this need, we have developed a carbon consumption database for the Continental United States (CONUS) and are developing a global carbon consumption database, both of which are directly link to distinct fuels within various fire danger categories. Fuel Characteristic Classification System (FCCS) fuels are used to parameterize biomass, and consumption is broken down into five Fire Danger categories (Low, Moderate, High, Very High, Extreme), for both ‘new’ and ‘residual’ burning scenarios. Residual burned area is defined as burning in areas that have recently burned. We implemented the FCCS 30-meter CONUS fuelbed dataset during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to estimate daily carbon emissions. Our emissions estimates were rigorously compared against in-situ measurements of CO2, CO, and black carbon aerosols, revealing a robust agreement between the two datasets. The 300-meter global database builds upon the foundations of the Pettinari, M. Lucrecia (2015) Global Fuelbed database, a global fuel map with standardized FCCS biomass parameters. These products can serve as valuable tools when used in conjunction with burned area data to rapidly and accurately estimate carbon consumed and released into the atmosphere.

FCCS↗
NASA NTRS1992

On the distribution of pitch angles in external galactic spirals NGC 1232 and NGC 5457

A numerical method, originally developed to analyze the morphology of global and local structure in prototype galaxies, is modified for analyzing observed disk-shape galaxies. Two digitized spiral galaxies NGC 1232 and NGC 5457 with varying degrees of contrast between arm and interarm regions are analyzed. A synergism of partitioning methods and a geometric mean least-squares regression algorithm serves to isolate local arm segments, spurs, feathers, and secondary features and to measure their pitch angles and lengths. The global arms are actually highly disjointed, with arm segments frequently revealing pitch angles between 30 and 50 deg, certainly greater than those of the parent arms. Prominent spurs tend to exhibit a much greater pitch angle. The automated mathematical algorithm is shown to have negligible numerical biasing and could be applied to any number of spiral galaxies manifesting flocculent structure, either prototype or observed, and could possibly be used as a tool for classification of multiple-armed-type galaxies.

Russell, William S.↗
DOE OSTI2023

Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders *

Abstract Recent advances in scanning tunneling and transmission electron microscopies (STM and STEM) have allowed routine generation of large volumes of imaging data containing information on the structure and functionality of materials. The experimental data sets contain signatures of long-range phenomena such as physical order parameter fields, polarization, and strain gradients in STEM, or standing electronic waves and carrier-mediated exchange interactions in STM, all superimposed onto scanning system distortions and gradual changes of contrast due to drift and/or mis-tilt effects. Correspondingly, while the human eye can readily identify certain patterns in the images such as lattice periodicities, repeating structural elements, or microstructures, their automatic extraction and classification are highly non-trivial and universal pathways to accomplish such analyses are absent. We pose that the most distinctive elements of the patterns observed in STM and (S)TEM images are similarity and (almost-) periodicity, behaviors stemming directly from the parsimony of elementary atomic structures, superimposed on the gradual changes reflective of order parameter distributions. However, the discovery of these elements via global Fourier methods is non-trivial due to variability and lack of ideal discrete translation symmetry. To address this problem, we explore the shift-invariant variational autoencoders (shift-VAEs) that allow disentangling characteristic repeating features in the images, their variations, and shifts that inevitably occur when randomly sampling the image space. Shift-VAEs balance the uncertainty in the position of the object of interest with the uncertainty in shape reconstruction. This approach is illustrated for model 1D data, and further extended to synthetic and experimental STM and STEM 2D data. We further introduce an approach for training shift-VAEs that allows finding the latent variables that comport to known physical behavior. In this specific case, the condition is that the latent variable maps should be smooth on the length scale of the atomic lattice (as expected for physical order parameters), but other conditions can be imposed. The opportunities and limitations of the shift VAE analysis for pattern discovery are elucidated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Drop Size Distribution Variability in Central Argentina during RELAMPAGO-CACTI

The Remote sensing of Electrification, Lightning, And Meso-scale/micro-scale Processes with Adaptive Ground Observations (RELAMPAGO) and the Cloud, Aerosol, and Complex Terrain Interactions Experiment Proposal (CACTI) field campaigns provided an unprecedented thirteen-disdrometer dataset in Central Argentina during the Intensive (IOP, 15 November to 15 December 2018) and Extended (EOP, 15 October 2018 to 30 April 2019) Observational Periods. The drop size distribution (DSD) parameters and their variability were analyzed across the region of interest, which was divided into three subregions characterized by the differing proximity to the Sierras de Córdoba (SDC), in order to assess the impact of complex terrain on the DSD parameters. A rigorous quality control of the data was first performed. The frequency distributions of DSD-derived parameters were analyzed, including the normalized intercept parameter (logN w ), the mean volume diameter (D 0 ), the mean mass diameter (D m ), the shape parameter (μ), the liquid water content (LWC), and the rain rate (R). The region closest to the SDC presented higher values of logN w , lower D 0 , and higher μ, while the opposite occurred in the farthest region, i.e., the concentration of small drops decreased while the concentration of bigger drops increased with the distance to the east of the SDC. Furthermore, the region closest to the SDC showed a bimodal distribution of D 0 : the lower values of D 0 were associated with higher values of logN w and were found more frequently during the afternoon, while the higher D 0 were associated with lower logN w and occurred more frequently during the night. The data were analyzed in comparison to the statistical analysis of Dolan et al. 2018 and sorted according to the classification proposed in the cited study. The logN w -D 0 and LWC-D 0 two-dimensional distributions allowed further discussion around the applicability of other mid-latitude and global precipitation classification schemes (startiform/convection) in the region of interest. Finally, three precipitation case studies were analyzed with supporting polarimetric radar data in order to relate the DSD characteristics to the precipitation type and the microphysical processes involved in each case.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

‘ Candidatus Dichloromethanomonas elyunquensis’ gen. nov., sp. nov., a dichloromethane-degrading anaerobe of the Peptococcaceae family

Taxonomic assignments of anaerobic dichloromethane (DCM)-degrading bacteria remain poorly constrained but are important for understanding the microbial diversity of organisms contributing to DCM turnover in environmental systems. Here, we describe the taxonomic classification of a novel DCM degrader in consortium RM obtained from pristine Rio Mameyes sediment. Phylogenetic analysis of full-length 16S rRNA gene sequences demonstrated that the DCM degrader was most closely related to members of the genera Dehalobacter and Syntrophobotulus, but sequence similarities did not exceed 94% and 93%, respectively. Genome-aggregate average amino acid identities against Peptococcaceae members did not exceed 66%, suggesting that the DCM degrader does not affiliate with any described genus. Phylogenetic analysis of conserved single-copy functional genes supported that the DCM degrader represents a novel clade. Growth strictly depended on the presence of DCM, which was consumed at a rate of 160 ± 3 μmol L -1 d -1 . The DCM degrader attained 5.25 × 10 7 ± 1.0 × 10 7 cells per μmol DCM consumed. Fluorescence in situ hybridization revealed rod-shaped cells 4 ± 0.8 μm long and 0.4 ± 0.1 μm wide. Furthermore, based on the unique phylogenetic, genomic, and physiological characteristics, we propose that the DCM degrader represents a new genus and species, ‘Candidatus Dichloromethanomonas elyunquensis’.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Global Carbon Consumption Database for Wildland Fire

Fire plays a significant role on both national and global scales, profoundly impacting landscapes shaped by human activity as well as those left wild. Even though fire can be devastating, wildland fire is a natural and integral force on our landscapes. Fires can also serve to reduce fuels to mitigate wildfire risk and maintain healthy ecosystem functions. However, the smoke produced by fires, regardless of their size or purpose, can pose adverse effects on human health when inhaled downwind. Understanding the influence of smoke on air quality and human well-being necessitates the quantification of emissions that fires release into the atmosphere. In response to this need, we have established a comprehensive global consumption database directly linked to distinct fuels within various fire danger categories. This database, featuring a spatial resolution of approximately 300 meters, builds upon the foundations of the Pettinari, M. Lucrecia (2015) Global Fuelbed database, a global fuel map with standardized Fuel Characteristic Classification System (FCCS) biomass parameters. Consumption is broken down into five Fire Danger categories (Low, Moderate, High, Very High, Extreme), for both ‘new’ and ‘residual’ burning scenarios. We define ‘residual burned area’ as area burning in a region that has burned on a previous day for the same fire season, and ‘new burned area’ as area burning in a region that has not recently burned. This product serves as a valuable tool when used in conjunction with burned area data to rapidly estimate the carbon consumed and released into the atmosphere. Previously, we developed a similar emissions method utilizing satellite information, in conjunction with the FCCS 30-meter United States fuelbed dataset. We implemented this approach on fires, documented during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to estimate daily carbon emissions. Our emissions estimates were rigorously compared against in-situ measurements of CO2, CO, and black carbon aerosols, revealing a robust agreement between the two datasets.

Emily Gargulinski↗
DOE OSTI2017

Automated Morphological and Morphometric Analysis of Mass Spectrometry Imaging Data: Application to Biomarker Discovery

Mass spectrometry imaging datasets are mostly analyzed in terms of average intensity in regions of interest. However, biological tissues have different morphologies with several sizes, shapes, and structures. The important biological information, contained in this highly heterogeneous cellular organization, could be hidden by analyzing the average intensities. Finding an analytical process of morphology would help to find such information, describe tissue model, and support identification of biomarkers. This study describes an informatics approach for the extraction and identification of mass spectrometry image features and its application to sample analysis and modeling. For the proof of concept, two different tissue types (healthy kidney and CT-26 xenograft tumor tissues) were imaged and analyzed. A mouse kidney model and tumor model were generated using morphometric – number of objects and total surface – information. The morphometric information was used to identify m/z that have a heterogeneous distribution. It seems to be a worthwhile pursuit as clonal heterogeneity in a tumor is of clinical relevance. This study provides a new approach to find biomarker or support tissue classification with more information. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Explainable Machine Learning for Functional Data

Black-box machine learning models are recognized as useful tools for prediction applications, but the algorithmic complexity of some models causes interpretation challenges. Explainability methods have been proposed to provide insight into these models, but there is little research focused on supervised modeling with functional data inputs. We argue that, especially in applications of high consequence, it is important to explicitly model the functional dependence in a black-box analysis to not obscure or misrepresent patterns in explanations. As such, we propose the V ariable importance E xplainable E lastic S hape A nalysis (VEESA) pipeline for training supervised machine learning models with functional inputs. The pipeline is an analysis process that includes the data preprocessing, modeling, and post-hoc explanations. The preprocessing is done using elastic functional principal components analysis, which accounts for vertical and horizontal variability in functional data and, ultimately, allows for explanations in the original data space that identify the important functional variability without bias due to correlated variables. Here, we demonstrate the pipeline on two high-consequence applications: explosives classification for national security and inkjet printer identification in forensic science. The applications exhibit the VEESA pipeline’s ability to provide an understanding of the characteristics of the functional data useful for prediction. Code for implementing the pipeline is available in the veesa R package (and supplemental python code).

Elastic Shape Analysis↗
DOE OSTI2022

Automating neutron resonances classification with Machine Learning [Slides]

Team reported the following accomplishments: the development of a Machine-Learning method to properly assign spins to neutron resonances (automated, general, reproducible); full integration with evaluated resonances in the Atlas (automation of new editions); training and optimization in synthetic data; validation and deployment to real experimental resonances. Future perspectives include exploration of other classifiers and hyper-parameter combinations, further validation with well-known nucleus (e.g. 235 U), and publication pipeline.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2022

A Regional Classification of Time Spectral Amplitudes in Total Electron Content: Southeastern United States During Solar Cycle 24

To investigate the meso-scale structure (100 s of km) of the ionosphere at mid-latitudes, the spectral properties in calculated total electron content (TEC) at a cluster of GPS receivers in and around Florida are analyzed. The ionosphere does not respond exactly the same to periodic solar driving at different locations around the planet, due to the complex electrodynamic interactions of the coupled magnetosphere-ionosphere system. Therefore, at each GPS receiver in the cluster we compare spatio-temporal variations of the spectral amplitudes for diurnal, solar rotation, and seasonal oscillations. The amplitudes for these dominant oscillations are organized with respect to magnetic latitude of the receiver. A low-latitude and high-latitude station are also included to put the mid-latitude ionospheric response into a global context. The amplitudes of diurnal, seasonal, and solar rotation signals are well ordered by magnetic latitude, superposed with meso-scale deviations between stations separated by ~100s of km. The results suggest that spatio-temporal variations of spectral amplitudes in the mid-latitude ionosphere are not dominated by a single process. This conclusion is based on our finding that at high latitude, the shape of the diurnal signal varies significantly less with solar activity compared to low- and mid-latitudes, and additionally, that the ratio of annual to semi-annual amplitudes fluctuates around 1 with time and from station-to-station only at mid-latitudes.

ionosphere↗
DOE OSTI2021

Whole-Genome Comparisons of Ergot Fungi Reveals the Divergence and Evolution of Species within the Genus Claviceps Are the Result of Varying Mechanisms Driving Genome Evolution and Host Range Expansion

The genus Claviceps has been known for centuries as an economically important fungal genus for pharmacology and agricultural research. Only recently have researchers begun to unravel the evolutionary history of the genus, with origins in South America and classification of four distinct sections through ecological, morphological, and metabolic features (Claviceps sects. Citrinae, Paspalorum, Pusillae, and Claviceps). The first three sections are additionally characterized by narrow host range, whereas section Claviceps is considered evolutionarily more successful and adaptable as it has the largest host range and biogeographical distribution. However, the reasons for this success and adaptability remain unclear. Our study elucidates factors influencing adaptability by sequencing and annotating 50 Claviceps genomes, representing 21 species, for a comprehensive comparison of genome architecture and plasticity in relation to host range potential. Our results show the trajectory from specialized genomes (sects. Citrinae and Paspalorum) toward adaptive genomes (sects. Pusillae and Claviceps) through colocalization of transposable elements around predicted effectors and a putative loss of repeat-induced point mutation resulting in unconstrained tandem gene duplication coinciding with increased host range potential and speciation. Alterations of genomic architecture and plasticity can substantially influence and shape the evolutionary trajectory of fungal pathogens and their adaptability. Furthermore, our study provides a large increase in available genomic resources to propel future studies of Claviceps in pharmacology and agricultural research, as well as, research into deeper understanding of the evolution of adaptable plant pathogens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters

The ability to generate physically plausible ensembles of variable sources is critical to the optimization of time domain survey cadences and the training of classification models on data sets with few to no labels. Traditional data augmentation techniques expand training sets by reenvisioning observed exemplars, seeking to simulate observations of specific training sources under different (exogenous) conditions. Unlike fully theory-driven models, these approaches do not typically allow principled interpolation nor extrapolation. Moreover, the principal drawback of theory-driven models lies in the prohibitive computational cost of simulating source observables from ab initio parameters. In this work, we propose a computationally tractable machine learning approach to generate realistic light curves of periodic variables capable of integrating physical parameters and variability classes as inputs. Our deep generative model, inspired by the transparent latent space generative adversarial networks, uses a variational autoencoder (VAE) architecture with temporal convolutional network layers, trained using the OGLE-III optical light curves and physical characteristics (e.g., effective temperature and absolute magnitude) from Gaia DR2. A test using the temperature–shape relationship of RR Lyrae demonstrates the efficacy of our generative “physics-enhanced latent space VAE” (PELS-VAE) model. Such deep generative models, serving as nonlinear nonparametric emulators, present a novel tool for astronomers to create synthetic time series over arbitrary cadences.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Mycorrhiza better predict soil fungal community composition and function than aboveground traits in temperate forest ecosystems

We sampled soil in the organic and mineral horizons beneath two AM-associated (Fraxinus americana, Thuja occidentalis) and two ECM-associated tree species (Betula alleghaniensis, and Tsuga canadensis), with an evergreen and deciduous species in each mycorrhizal group. To characterize fungal communities and organic matter decomposition beneath each tree species, we sequenced the ITS1 region of fungal DNA and measured the potential activity of carbon and nitrogen-targeting extracellular enzymes. Each tree species harbored distinct fungal communities, supporting the need to consider both mycorrhizal type and leaf habit. However, between tree characteristics, mycorrhizal type better predicted fungal communities. Across fungal guilds, saprotrophic fungi were the most important group in shaping fungal community differences in soils beneath all tree species. The effect of leaf habit on carbon and nitrogen-targeting hydrolytic enzymes depended on tree mycorrhizal association in the organic horizon, while oxidative enzyme activities were higher beneath EcM-associated trees across both soil horizons and leaf habits. These data include extracellular hydrolytic and oxidative enzyme activities, ITS sequencing fungal community data, soil carbon to nitrogen, and soil pH. Site level data include climate (mean annual temperature, precipitation), elevation, and soil series and order information.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Application of Support Vector Regression to Derive Crater Depth/Diameter From Satellite Images

Through the study of impact crater shapes, one can draw important conclusions about the nature and evolution of planetary surfaces [e.g., 1-4].In particular, studying the depth (d) to diameter (D)ratio (d/D) of a population of impact craters, in combination with crater count statistics, can yield valuable insights regarding rates of erosion and burial[5]. Motivated by the great abundance of available planetary surface image data, the goal of this project is to develop an efficient way to estimate d/D from satellite images of impact craters for which stereo information is not available [6]. We set out to develop and train a machine learning algorithm to extract d/D from a dataset of synthetic impact crater images for which model d/D is known. The applications of machine learning to planetary science are numerous and diverse [7], including automatic planetary surface mapping [8] and the detection of impact craters [9]. Our algorithm makes use of Support Vector Regression (SVR), which is a type of Support Vector Machine (SVM) [10, 11].SVMs are a branch of supervised machine learning valued for their straightforward implementation and versatility in solving both classification and regression problems. In regression analysis, an SVR algorithm produces a hyperplane function to fit the training data points, as well as an ε-tube that surrounds the hyperplane. Tunable hyperparameters include the width of the ε-tube (ε) and the amount an algorithm is penalized for points which fall outside the ε-tube.

L R Chin↗