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Results for “principal components analysis”

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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

In situ molecular imaging of adsorbed protein films in water indicating hydrophobicity and hydrophilicity

Abstract In situ molecular imaging of protein films adsorbed on a solid surface in water was realized by using a vacuum compatible microfluidic interface and time-of-flight secondary ion mass spectrometry (ToF-SIMS). Amino acid fragments from such hydrated protein films are observed and identified in the positive ion mode and the results are in agreement with reported works on dry protein films. Moreover, water clusters from the hydrated protein films have been observed and identified in both the positive and negative ion mode for a series protein films. Thus, the detailed composition of amino acids and water molecules in the hydrated protein films can be characterized, and the protein water microstructures can be revealed by the distinct three-dimensional spatial distribution reconstructed from in situ liquid ToF-SIMS molecular imaging. Furthermore, spectral principal component analysis of amino acid fragment peaks and water cluster peaks provides unique insights into the water cluster distribution, hydrophilicity, and hydrophobicity of hydrated adsorbed protein films in water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel

Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Reductive catalytic fractionation of agricultural residue and energy crop lignin and application of lignin oil as antimicrobials

Cheap and abundant waste from bioethanol and agricultural processing industries are an alluring alternative feedstock for biorefineries. In this work, we employ reductive catalytic fractionation (RCF) to depolymerize, over Ru/C powder and Ru/Al 2 O 3 pellets, five herbaceous biomass feedstocks, namely corn stover, miscanthus, switchgrass, sugarcane bagasse, and wheat straw into phenolic monomers with high yields (~40 wt.% based on total (Klason + acid soluble) lignin and > 50 wt.% when stabilized using aldehydes), leaving behind a carbohydrate pulp residue. Interestingly, a polar solvent is sufficient to solubilize and fragment the lignin polymer into monomers without any catalyst. Here, contrary to woody biomass, where the monomer yields are positively correlated with the S-content of lignin, principal component analysis indicates that the monomer yields from herbaceous biomass depend on the content of lignin crosslinker – ferulate. Using NMR, we further identify α – 6 C-C linked oligomers formed from condensation reactions, explaining the unexpected low monomer yields of high β-O-4 herbaceous biomass. Recyclability experiments indicate that catalyst deactivation occurs through sintering, leaching, and fouling. Lignin oil from herbaceous biomass exhibits bacteriostatic effects against Staphylococcus aureus, highlighting a potential application in functional food development and as food or feed preservative.

09 BIOMASS FUELS↗
DOE OSTI2020

Combined Plasmonic Au-nanoparticle and Conducting Metal Oxide High-temperature Optical Sensing with LSTO

<span style="color: rgba(0, 0, 0, 0.79); font-family: &quot;Source Sans Pro&quot;, source-sans-pro, museo_sans300, museo-sans, Arial, sans-serif; font-size: 16px; background-color: rgb(255, 255, 255);">Fiber optic sensor technology offers several advantages for harsh-environment applications. However, the development of optical gas sensing layers that are stable under harsh environmental conditions is an ongoing research challenge. In this work, electronically conducting metal oxide lanthanum-doped strontium titanate (LSTO) films embedded with gold nanoparticles are examined as a sensing layer for application in reducing gas flows at high temperature (600–800 °C). A strong localized surface plasmon resonance (LSPR) based response to hydrogen is demonstrated in the visible region of the spectrum, while a Drude free electron-based response is observed in the near-IR. Characteristics of these responses are studied both on planar glass substrates and on silica fibers. Charge transfer between the oxide film and the gold nanoparticles is explored as a possible mechanism governing the Au LSPR response and is considered in terms of the corresponding properties of the conducting metal oxide-based matrix phase. Principal component analysis is applied to the combined plasmonic and free-carrier based response over a range of temperatures and hydrogen concentrations. It is demonstrated that the combined visible and near-IR response of these films provides improved versatility for multiwavelength interrogation, as well as improved discrimination of important process parameters (concentration and temperature) through application of multivariate analysis techniques.</span>

Wuenschell, Jeffrey K.↗
DOE OSTI2020

Active Learning-driven Quantitative Synthesis-Structure-Property Relations for Improving Performance and Revealing Active Sites of Nitrogen-Doped Carbon for the Hydrogen Evolution Reaction

While quantitative structure-properties relations (QSPRs) have been developed successfully in multiple fields, catalyst synthesis affects structure and in turn performance, making simple QSPRs inadequate. Furthermore, catalysts often have multiple active sites preventing one from obtaining insights into structure-property relations. Here, we develop a data-driven quantitative synthesis-structure-property relation (QS2PRs) methodology to elucidate correlations between catalyst synthesis conditions, structural properties as well as observed performance and to provide fundamental insights into active sites and a systematic way to optimize practical catalysts. Here, we demonstrate the approach to the synthesis of nitrogen-doped catalysts (NDC) made via pyrolysis for the performance of the electrochemical hydrogen evolution reaction (HER), quantified by the onset potential and the current density. We determine crystallinity, nitrogen species type and fraction, surface area, and pore structure of the NDC’s using XRD, XPS, and BET characterization. We demonstrated that an active learning-based optimization combined with various elementary machine learning tools (regression, principal component analysis, partial least squares) can efficiently identify optimum pyrolysis conditions to tune structural characteristics and performance with concomitant savings in materials and experimental time. Unlike previous reports on the importance of pyridinic or graphitic nitrogen, we discover that the electrochemical performance is not driven by a single catalyst property; rather, it arises from a multivariate influence of nitrogen dopants, pore structure and disorder in the NDC materials. Identification of active sites can help mechanistic understanding and further catalyst improvement.

42 ENGINEERING↗
DOE OSTI2021

Molecular imaging of plant-microbe interactions on the Brachypodium seed surface

Plant growth-promoting rhizobacteria (PGPR) play a crucial role in biological control and pathogenic defense on and with-in plant tissues, however the mechanism(s) by which plants associate with PGPR to elicit such beneficial effects needs further study. Here, we present high mass resolution time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging of Brachypodium distachyon (Brachypodium) seeds with and without exposure to two model PGPR, i.e., Gram-negative Pseudomonas fluorescens SBW25::GFP (P.) and Gram-positive Arthrobacter chlorophenolicus A6::GFP (A.). Delayed image extraction was used to image PGPB-treated seed sections to reveal morphological changes. ToF-SIMS spectral compari-son, principal component analysis (PCA), and two-dimensional (2D) imaging show that the selected PGPR have different effects on the host seed surface, resulting in changes in chemical composition and morphology. Metabolite prod-ucts and biomarkers, such as flavonoids, phenolic com-pounds, fatty acids, and indole-3-acetic acid (IAA), were ob-served on the PGPR-treated seed surfaces. These compounds had different distributions on the Brachypodium seed surface for the two PGPR, indicating that the different bacteria elicited distinct responses from the host. Our results illustrate that ToF-SIMS is an effective tool to study plant-microbe interac-tions and to provide insightful information with submicrome-ter lateral resolution of the chemical distributions associated with morphological features, potentially offering a new way to study the mechanisms underlying beneficial roles of PGPR.

PGPR, Brachypodium distachyon, ToF-SIMS, metabolit↗
DOE OSTI2021

Unsupervised machine learning for unbiased chemical classification in X-ray absorption spectroscopy and X-ray emission spectroscopy

Here we report a comprehensive computational study of unsupervised machine learning for extraction of chemically relevant information in X-ray absorption near edge structure (XANES) and in valence-to-core X-ray emission spectra (VtC-XES) for classification of a broad ensemble of sulphorganic molecules. By progressively decreasing the constraining assumptions of the unsupervised machine learning algorithm, moving from principal component analysis (PCA) to a variational autoencoder (VAE) to t-distributed stochastic neighbour embedding (t-SNE), we find improved sensitivity to steadily more refined chemical information. Surprisingly, when embedding the ensemble of spectra in merely two dimensions, t-SNE distinguishes not just oxidation state and general sulphur bonding environment but also the aromaticity of the bonding radical group with 87% accuracy as well as identifying even finer details in electronic structure within aromatic or aliphatic sub-classes. We find that the chemical information in XANES and VtC-XES is very similar in character and content, although they unexpectedly have different sensitivity within a given molecular class. We also discuss likely benefits from further effort with unsupervised machine learning and from the interplay between supervised and unsupervised machine learning for X-ray spectroscopies. Our overall results, i.e., the ability to reliably classify without user bias and to discover unexpected chemical signatures for XANES and VtC-XES, likely generalize to other systems as well as to other one-dimensional chemical spectroscopies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A protein scaffold enables hydrogen evolution for a Ni-bisdiphosphine complex

We find that an artificial metalloenzyme acting as a functional biomimic of hydrogenase enzymes was activated by assembly via covalent attachment of the molecular complex, [Ni(PN glycine P) 2 ] 2- , within a structured protein scaffold. Electrocatalytic H2 production was observed from pH 3.0 to 10.0 for the artificial enzyme, while no electrocatalytic activity was observed for similar [Ni(PNP) 2 ] 2+ systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Systematic characterization of selenium speciation in coal fly ash

Millions of tons of coal fly ashes (CFAs) are produced annually during coal combustion in the U.S., which are commonly beneficially used in the concrete industry or disposed of in ash ponds. CFAs contain trace amounts of a range of toxic heavy metals including selenium (Se). Because the toxicity of Se is dependent on its speciation, investigating Se speciation in CFAs as affected by coal source and combustion conditions can help understand the related environmental and human health impacts during disposal or beneficial reuse. In this study, a set of representative CFA samples were characterized for Se speciation using synchrotron X-ray absorption spectroscopy (XAS) and micro-X-ray fluorescence spectromicroscopy (μ-XRF/XAS). Se-containing particles were highly heterogeneous, and individual particles might contain multiple oxidation states including Se(0), Se(IV), and Se(VI). Principal component analysis was performed for sample characteristics including Al 2 O 3 , SiO 2 , CaO, FeO, loss on ignition, average particle size, Se concentration, and Se oxidation state. Selective catalytic reduction (SCR), which is used to limit nitrogen oxide (NO x ) emissions during coal combustion, was found to be associated with the presence of reduced Se oxidation states, with up to 90% Se(0) observed in samples with SCR. Alongside SCR, FeO content may also influence Se speciation.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Machine learning inversion from small-angle scattering for charged polymers

We develop Monte Carlo simulations for uniformly charged polymers and a machine learning algorithm to interpret the intra-polymer structure factor of the charged polymer system, which can be obtained from small-angle scattering experiments. The polymer is modeled as a chain of fixed-length bonds, where the connected bonds are subject to bending energy, and there is also a screened Coulomb potential for charge interaction between all joints. The bending energy is determined by the intrinsic bending stiffness, and the charge interaction depends on the interaction strength and screening length. All three contribute to the stiffness of the polymer chain and lead to longer and larger polymer conformations. The screening length also introduces a second length scale for the polymer besides the bending persistence length. To obtain the inverse mapping from the structure factor to these polymer conformation and energy-related parameters, we generate a large data set of structure factors by running simulations for a wide range of polymer energy parameters. We use principal component analysis to investigate the intra-polymer structure factors and determine the feasibility of the inversion using the nearest neighbor distance. We employ Gaussian process regression to achieve the inverse mapping and extract the characteristic parameters of polymers from the structure factor with low relative error.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Remote sensing of Pu in uranyl nitrate crystals using reflectance spectroscopy and chemometrics

Remote quantification of Pu(VI) (0–5 mol%) co-crystallized with U in uranyl nitrate hexahydrate (UNH) crystals was achieved in a glove box using reflectance spectroscopy coupled with chemometric modeling. Reflectance spectra were also acquired for Pu(IV) and Np(VI) (0–5 mol%) crystallized with UNH; revealing spectral features consistent with their solution-phase analogs. Principal component analysis revealed Pu(IV/VI) and Np(VI) concentrations as the primary source of variation in the data, informing the development of a supervised partial least squares regression model for Pu(VI). The resulting calibration demonstrated robust performance, with replicate root mean square errors near 10% and quantifiable limits near 0.2 mol% Pu(VI) relative to U. The Pu(VI) remained stable in the crystalline UNH matrix for at least one week with minimal reduction to Pu(IV). Notably, Pu(VI) and Np(VI) incorporation in UNH quenched U(VI) fluorescence while Pu(IV) did not. This study presents a noninvasive, spectroscopic approach for solid-state Pu quantification, with direct implications for material accountability and nuclear nonproliferation monitoring.

Sadergaski, Luke R. [Oak Ridge National Laboratory↗
DOE OSTI2025

Effects of chemical composition and physicochemical properties of poplar biomass on the performance of 3D printed poplar-reinforced PLA materials

Lignocellulosic biomass has been well-acknowledged as a filler for making 3D printed composites. The technical performances of composites were influenced by the characteristics of the components. The correlations between poplar biomass properties and the mechanical and thermal performances of the 3D printed poplar-plastic composites were investigated. The characteristics of poplar were modified by different pretreatment methods, including using hot water, dilute acid, and organic solvent (organosolv), and each treated poplar biomass was applied as a filler in a polylactic acid (PLA) polymer matrix to produce eco-friendly materials. These solvent pretreatments increased the hydrophobicity and surface area of poplar. Organosolv treated poplar showed the highest cellulose content and significantly increased Young's modulus of its biocomposites. Principal component analysis revealed that the specific surface area and water contact angle of biomass contributed to the thermal stability of biocomposites. Additionally, the degree of polymerization of cellulose and xylan content within the biomass correlated with the biocomposites' break stress. Notably, the crystallinity of biocomposites impacted the modulus of these materials. The reported relationships between biomass characteristics and 3D printed composite behaviors provide guidance for optimizing biomass processing in biocomposite applications.

Ji, Anqi [State Univ. of New York (SUNY), Syracuse↗
DOE OSTI2021

Erratum: Planck 2018 results: VI. Cosmological parameters

In the original version, the bounds given in Eqs. (87a) and (87b) on the contribution to the early-time optical depth, (15,30), contained a numerical error in deriving the 95th percentile from the Monte Carlo samples. The corrected 95% upper bounds are: τ(15,30) < 0:018 (lowE, flat τ(15, 30), FlexKnot), (1) τ(15, 30) < 0:023 (lowE, flat knot, FlexKnot): (2) These bounds are a factor of 3 larger than the originally reported results. Consequently, the new bounds do not significantly improve upon previous results from Planck data presented in Millea & Bouchet (2018) as was stated, but are instead comparable. Equations (1) and (2) give results that are now similar to those of Heinrich & Hu (2021), who used the same Planck 2018 data to derive a 95% upper bound of 0.020 using the principal component analysis (PCA) model and uniform priors on the PCA mode amplitudes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The chemical composition of globular clusters in the Local Group

We present detailed chemical abundance measurements for 45 globular clusters (GCs) associated with galaxies in (and, in one case, beyond) the Local Group. The measurements are based on new high-resolution integrated-light spectra of GCs in the galaxies NGC 185, NGC 205, M 31, M 33, and NGC 2403, combined with reanalysis of previously published observations of GCs in the Fornax dSph, WLM, NGC 147, NGC 6822, and the Milky Way. The GCs cover the range -2.8 < [Fe/H] < -0.1 and we determined abundances for Fe, Na, Mg, Si, Ca, Sc, Ti, Cr, Mn, Ni, Cu, Zn, Zr, Ba, and Eu. Corrections for non local thermodynamic equilibrium effects are included for Na, Mg, Ca, Ti, Mn, Fe, Ni, and Ba, building on a recently developed procedure. For several of the galaxies, our measurements provide the first quantitative constraints on the detailed composition of their metal-poor stellar populations. Overall, the GCs in different galaxies exhibit remarkably uniform abundance patterns of the α, iron-peak, and neutron-capture elements, with a dispersion of less than 0.1 dex in [α/Fe] for the full sample. There is a hint that GCs in dwarf galaxies are slightly less α-enhanced (by ~0.04 dex on average) than those in larger galaxies. One GC in M 33 (HM33-B) resembles the most metal-rich GCs in the Fornax dSph (Fornax 4) and NGC 6822 (SC7) by having α-element abundances closer to scaled-solar values, possibly hinting at an accretion origin. A principal components analysis shows that the α-element abundances strongly correlate with those of Na, Sc, Ni, and Zn. Several GCs with [Fe/H] < -1.5 are deficient in Mg compared to other α-elements. We find no GCs with strongly enhanced r-process abundances as reported for metal-poor stars in some ultra-faint dwarfs and the Magellanic Clouds. The similarity of the abundance patterns for metal-poor GCs in different environments points to similar early enrichment histories and only allow for minor variations in the initial mass function.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Dark Energy Survey Deep Field photometric redshift performance and training incompleteness assessment

Context. The determination of accurate photometric redshifts (photo-zs) in large imaging galaxy surveys is key for cosmological studies. One of the most common approaches are machine learning techniques. These methods require a spectroscopic or reference sample to train the algorithms. Attention has to be paid to the quality and properties of these samples since they are key factors in the estimation of reliable photo-zs. Aims. The goal of this work is to calculate the photo-zs for the Y3 DES Deep Fields catalogue using the DNF machine learning algorithm. Moreover, we want to develop techniques to assess the incompleteness of the training sample and metrics to study how incompleteness affects the quality of photometric redshifts. Finally, we are interested in comparing the performance obtained with respect to the EAzY template fitting approach on Y3 DES Deep Fields catalogue. Methods. We have emulated -- at brighter magnitude -- the training incompleteness with a spectroscopic sample whose redshifts are known to have a measurable view of the problem. We have used a principal component analysis to graphically assess incompleteness and to relate it with the performance parameters provided by DNF. Finally, we have applied the results about the incompleteness to the photo-z computation on Y3 DES Deep Fields with DNF and estimated its performance. Results. The photo-zs for the galaxies on DES Deep Fields have been computed with the DNF algorithm and added to the Y3 DES Deep Fields catalogue. They are available at https://des.ncsa.illinois.edu/releases/y3a2/Y3deepfields. Some techniques have been developed to evaluate the performance in the absence of "true" redshift and to assess completeness. We have studied... (Partial abstract)

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Manifold projection image segmentation for nano-XANES imaging

As spectral imaging techniques are becoming more prominent in science, advanced image segmentation algorithms are required to identify appropriate domains in these images. We present a version of image segmentation called manifold projection image segmentation (MPIS) that is generally applicable to a broad range of systems without the need for training because MPIS uses unsupervised machine learning with a few physically motivated hyperparameters. We apply MPIS to nanoscale x-ray absorption near edge structure (XANES) imaging, where XANES spectra are collected with nanometer spatial resolution. We show the superiority of manifold projection over linear transformations, such as the commonly used principal component analysis (PCA). Moreover, MPIS maintains accuracy while reducing computation time and sensitivity to noise compared to the standard nano-XANES imaging analysis procedure. Finally, we demonstrate how multimodal information, such as x-ray fluorescence data and spatial location of pixels, can be incorporated into the MPIS framework. We propose that MPIS is adaptable for any spectral imaging technique, including scanning transmission x-ray microscopy, where the length scale of domains is larger than the resolution of the experiment.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Descriptors of water aggregation

For this work, we rely on a total of 23 (cluster size, 8 structural, and 14 connectivity) descriptors to investigate structural patterns and connectivity motifs associated with water cluster aggregation. In addition to the cluster size n (number of molecules), the 8 structural descriptors can be further categorized into (i) one-body (intramolecular): covalent OH bond length (r OH ) and HOH bond angle (θ HOH ), (ii) two-body: OO distance (r OO ), OHO angle (θ OHO ), and HOOX dihedral angle ($\phi$ HOOX ), where X lies on the bisector of the HOH angle, (iii) three-body: OOO angle (θ OOO ), and (iv) many-body: modified tetrahedral order parameter (q) to account for two-, three-, four-, five-coordinated molecules (q m , m = 2, 3, 4, 5) and radius of gyration (R g ). The 14 connectivity descriptors are all many-body in nature and consist of the AD, AAD, ADD, AADD, AAAD, AAADD adjacencies [number of hydrogen bonds accepted (A) and donated (D) by each water molecule], Wiener index, Average Shortest Path Length, hydrogen bond saturation (% HB), and number of non-short-circuited three-membered cycles, four-membered cycles, five-membered cycles, six-membered cycles, and seven-membered cycles. We mined a previously reported database of 4 948 959 water cluster minima for (H 2 O) n , n = 3–25 to analyze the evolution and correlation of these descriptors for the clusters within 5 kcal/mol of the putative minima. It was found that r OH and % HB correlated strongly with cluster size n, which was identified as the strongest predictor of energetic stability. Marked changes in the adjacencies and cycle count were observed, lending insight into changes in the hydrogen bond network upon aggregation. A Principal Component Analysis (PCA) was employed to identify descriptor dependencies and group clusters into specific structural patterns across different cluster sizes. The results of this study inform our understanding of how water clusters evolve in size and what appropriate descriptors of their structural and connectivity patterns are with respect to system size, stability, and similarity. The approach described in this study is general and can be easily extended to other hydrogen-bonded systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Augmenting machine learning of Grad–Shafranov equilibrium reconstruction with Green's functions

This work presents a method for predicting plasma equilibria in tokamak fusion experiments and reactors. The approach involves representing the plasma current as a linear combination of basis functions using principal component analysis of plasma toroidal current densities (J t ) from the EFIT-AI equilibrium database. Then utilizing EFIT's Green's function tables, basis functions are created for the poloidal flux (ψ) and diagnostics generated from the toroidal current (J t ). Similar to the idea of a physics-informed neural network (NN), this physically enforces consistency between ψ, J t , and the synthetic diagnostics. First, the predictive capability of a least squares technique to minimize the error on the synthetic diagnostics is employed. The results show that the method achieves high accuracy in predicting ψ and moderate accuracy in predicting J t with median R 2 = 0.9993 and R 2 = 0.978, respectively. A comprehensive NN using a network architecture search is also employed to predict the coefficients of the basis functions. The NN demonstrates significantly better performance compared to the least squares method with median R 2 = 0.9997 and 0.9916 for J t and ψ, respectively. The robustness of the method is evaluated by handling missing or incorrect data through the least squares filling of missing data, which shows that the NN prediction remains strong even with a reduced number of diagnostics. Additionally, the method is tested on plasmas outside of the training range showing reasonable results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗