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Results for “Constrained 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.
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SkyFACT: high-dimensional modeling of gamma-ray emission with adaptive templates and penalized likelihoods
We present SkyFACT (Sky Factorization with Adaptive Constrained Templates), a new approach for studying, modeling and decomposing diffuse gamma-ray emission. Like most previous analyses, the approach relies on predictions from cosmic-ray propagation codes like GALPROP and DRAGON. However, in contrast to previous approaches, we account for the fact that models are not perfect and allow for a very large number (∼> 10{sup 5}) of nuisance parameters to parameterize these imperfections. We combine methods of image reconstruction and adaptive spatio-spectral template regression in one coherent hybrid approach. To this end, we use penalized Poisson likelihood regression, with regularization functions that are motivated by the maximum entropy method. We introduce methods to efficiently handle the high dimensionality of the convex optimization problem as well as the associated semi-sparse covariance matrix, using the L-BFGS-B algorithm and Cholesky factorization. We test the method both on synthetic data as well as on gamma-ray emission from the inner Galaxy, |ℓ|<90{sup o} and | b |<20{sup o}, as observed by the Fermi Large Area Telescope. We finally define a simple reference model that removes most of the residual emission from the inner Galaxy, based on conventional diffuse emission components as well as components for the Fermi bubbles, the Fermi Galactic center excess, and extended sources along the Galactic disk. Variants of this reference model can serve as basis for future studies of diffuse emission in and outside the Galactic disk.
Frequency-Nadir-Constrained Unit Commitment for Low-Inertia, High-IBR Island Power Systems [Slides]
The process of energy decarbonization in island power systems is accelerated due to the swift integration of inverter-based renewable energy resources (IBRs). The unique features of such systems, including rapid frequency changes resulting from potential generation outages or imbalances due to the unpredictability of renewable power, pose a significant challenge in maintaining the frequency nadir without external support. This paper presents a unit commitment (UC) model with data-driven frequency nadir constraints, including either frequency nadir or minimum inertia requirements, helping to limit frequency deviations after significant generator outages. The constraints are formulated using a linear regression model that takes advantage of real-world, year-long generation scheduling and dynamic simulation data. The efficacy of the proposed UC model is verified through a year-long simulation in an actual island power system using historical weather data. The alternative minimum inertia constraint, derived from actual system operation assumptions, is also evaluated. Findings demonstrate that the proposed frequency nadir constraint notably improves the system's frequency nadir under high photovoltaic (PV) penetration levels, albeit with a slight increase in generation costs, when compared to the alternative minimum inertia constraint.
Probabilistic learning and updating of a digital twin for composite material systems
This paper presents an approach for characterizing and estimating statistical dependence between a large number of observables in a composite material system. Conditional regression is carried out using the estimated joint density function, permitting a systematic exploration of interdependence between fine scale and coarse observables that can be used for both prognosis and design of complex material systems. An example demonstrates the integration of experimental data with a computational database. The statistical approach is based on the probabilistic learning on manifolds recently developed by the authors. This approach leverages intrinsic structure detected through diffusion on graphs with projected stochastic differential equations to generate samples constrained to that structure.
On the Statistical Properties of the Lower Main Sequence
Astronomy is in an era where all-sky surveys are mapping the Galaxy. The plethora of photometric, spectroscopic, asteroseismic, and astrometric data allows us to characterize the comprising stars in detail. Here we quantify to what extent precise stellar observations reveal information about the properties of a star, including properties that are unobserved, or even unobservable. We analyze the diagnostic potential of classical and asteroseismic observations for inferring stellar parameters such as age, mass, and radius from evolutionary tracks of solar-like oscillators on the lower main sequence. We perform rank correlation tests in order to determine the capacity of each observable quantity to probe structural components of stars and infer their evolutionary histories. We also analyze the principal components of classic and asteroseismic observables to highlight the degree of redundancy present in the measured quantities and demonstrate the extent to which information of the model parameters can be extracted. We perform multiple regression using combinations of observable quantities in a grid of evolutionary simulations and appraise the predictive utility of each combination in determining the properties of stars. We identify the combinations that are useful and provide limits to where each type of observable quantity can reveal information about a star. We investigate the accuracy with which targets in the upcoming TESS and PLATO missions can be characterized. We demonstrate that the combination of observations from GAIA and PLATO will allow us to tightly constrain stellar masses, ages, and radii with machine learning for the purposes of Galactic and planetary studies.
The effects of He I λ10830 on helium abundance determinations
Observations of helium and hydrogen emission lines from metal-poor extragalactic H II regions, combined with estimates of metallicity, provide an independent method for determining the primordial helium abundance, Y{sub p}. Traditionally, the emission lines employed are in the visible wavelength range, and the number of suitable lines is limited. Furthermore, when using these lines, large systematic uncertainties in helium abundance determinations arise due to the degeneracy of physical parameters, such as temperature and density. Recently, Izotov, Thuan, and Guseva (2014) have pioneered adding the He I λ10830 infrared emission line in helium abundance determinations. The strong electron density dependence of He I λ10830 makes it ideal for better constraining density, potentially breaking the degeneracy with temperature. We revisit our analysis of the dataset published by Izotov, Thuan, and Stasi and apos;nska (2007) and incorporate the newly available observations of He I λ10830 by scaling them using the observed-to-theoretical Paschen-gamma ratio. The solutions are better constrained, in particular for electron density, temperature, and the neutral hydrogen fraction, improving the model fit to data, with the result that more spectra now pass screening for quality and reliability, in addition to a standard 95% confidence level cut. Furthermore, the addition of He I λ10830 decreases the uncertainty on the helium abundance for all galaxies, with reductions in the uncertainty ranging from 10–80%. Overall, we find a reduction in the uncertainty on Y{sub p} by over 50%. From a regression to zero metallicity, we determine Y{sub p} = 0.2449 ± 0.0040, consistent with the BBN result, Y{sub p} = 0.2470 ± 0.0002, based on the Planck determination of the baryon density. The dramatic improvement in the uncertainty from incorporating He I λ10830 strongly supports the case for simultaneous (thus not requiring scaling) observations of visible and infrared helium emission line spectra.
Geohydrodynamic Parameters and Their Implications on the Coastal Conservation: A Case Study of Abak Local Government Area (LGA), Akwa Ibom State, Southern Nigeria
A total of 14 vertical electrical soundings using Schlumberger electrode configuration and the complementary laboratory analysis of aquifer samples were carried out in the Abak Local Government Area of Akwa Ibom State, the coastal region of Nigeria. The study focused on the estimation of geohydrodynamic parameters of the frequently exploited aquifers and the implication of hydrodynamic parameters on the lithostratigraphy and the anticipated exposure of the assessed geologic formation at the shorelines. These parameters were porosity (ϕ), tortuosity (τ), formation factor (F), aquifer water formation resistivity (R{sub w}) and coefficient of permeability/hydraulic conductivity (K). Computation of the effective porosities from the aquifer cuttings was carried out using wet weight–dry weight technique and petrophysical techniques. The F values were computed using the aquifer formation bulk resistivity measured from field 1-D resistivity data analysis, whose interpretation was constrained by nearby borehole information. The formation pore water resistivities were estimated from the laboratory using electrical resistivity metre. The Win RESIST software program was used in interpreting the field data electronically. The results of interpretation gave the primary parameters of saturated and unsaturated units of the coastal regions used in this work. The area generally shows seemingly high porosity with high coefficient of permeability. The primary and secondary parameters have been contoured to model their distributions. Besides, some functional relations have been realized through regression analyses. The contour distribution of the geohydrodynamic parameters indicates the vulnerability of the water repositories to contaminations as well as the vulnerability of the shoreline to waterborne erosion. The seemingly high effective porosity in the compliant laboratory and calculated values indicate that the coastal region is neither lithified nor compacted/consolidated. This signals the possibility of the formation to be easily eroded, weathered or flooded where these units are exposed to water current. With these revelations, the shorelines could be properly managed and conserved by geotechnically reinforcing with hard and water-resistant concrete that can protect the vulnerable and erosion-prone porous sediments.
Approximate Mixed-Integer Programming Solution with Machine Learning Technique and Linear Programming Relaxation
Security constrained unit commitment (SCUC) is solved in the day-ahead electricity market to determine electricity generation schedule for the following date. The associated mixed integer programming (MIP) problem is known computationally hard. Heuristics like rounding can be computationally efficient for solving large MIP problems however often produces lower quality solutions due to discrepancy between MIP solution and linear programming relaxation (LPR) solution, i.e., variables taking different discrete values between MIP and LPR solutions. In this work, we applied the machine learning-based algorithms—Classification and Regression Tree (CART) model and random forest (RF) model—to obtain an LPR-based approximation that close to an MIP solution without actually solving the MIP problem. By computing feature importance derived from the CART and RF models, we assessed the link between coefficients in the MIP model, the LPR solution, and the optimal solution of the MIP problem. We applied the CART and RF approaches to solve SCUC problems using day-ahead energy market cases from Midcontinent Independent System Operator. The results show that our method is computationally efficient and could achieve an approximation sufficiently close to the SCUC solution with <0.15% error and ~78% reduction in the discrepancies between LPR and MIP solutions.
Event-Based Energy Impact Tracking and Forecasting with Limited Measurements for Rooftop Units
Packaged air conditioning units and heat pumps, also known as rooftop units (RTUs), are responsible for almost 133 billion kWh of electricity usage annually on site for space cooling U.S. commercial buildings. In addition, the use of heat pumps is a trend we expect to accelerate as buildings transition from fossil fuel-based heating to electricity as a key step for decarbonizing the U.S. commercial buildings sector. However, the operation conditions and energy use of RTUs and heat pumps are usually not well monitored as they are not commonly integrated with building automation systems and lack exposed sensing and control points. To fill this gap, this paper proposes a framework for tracking and forecasting energy impacts resulting from degradation of performance and improved performance for unit servicing using limited data. The proposed framework makes use of a constrained dataset, specifically measurements of the outdoor air temperature and the power demand of individual RTUs, to track and forecast changes in energy use associated with changes in performance over various temporal horizons ranging from days to weeks. Following the detection of an RTU fault, performance degradation, or performance improvement, the framework employs a prediction model to assess the cumulative energy impact. We demonstrate the effectiveness of the method with field-collected data for servicing and degradation examples and compare the predicting accuracy of Gradient Boosting Decision Tree (GBDT) Regression models to Support Vector Regression and Linear Regression models. The results show that GBDT achieved the best accuracy for time-series validation datasets for the servicing and degradation cases, and the prediction model was able to track the cumulative energy impacts of events. The proposed framework can inform building owners of the cumulative change in energy usage of RTUs associated with performance degradation, performance improvement, or a fault.
The solubility of titanite in silicate melt determined from growth and dissolution experiments
The solubility of titanite (CaTiSiO 5 ) in Si-rich melts was measured experimentally through growth experiments at 800–1000 °C, 0.5–1.0 GPa, log fO 2 ~ CCO–0.8, t = 72–168 h, and H 2 O = 0 to 4 wt.%, and in dissolution experiments at 925–1300 °C, 0.8 GPa, t = 18–118 h, and H 2 O = 1–10 wt.% in a piston-cylinder apparatus. Run product glasses in growth experiments were homogeneous, and iron loss suppressed ilmenite crystallization. Saturation concentrations in dissolution experiments were estimated by fitting measured diffusion profiles. Titanite solubility increases with increasing temperature and melt composition parameter \(M = {\text{ molar }}\left( {{\text{Na }} + {\text{ K }} + {\text{ 2Ca}}} \right)/\left( {{\text{Al }} \times {\text{ Si}}} \right)\) . Multiple linear regression of glass composition data from growth and dissolution experiments ( n = 29) plus 39 experiments from the LEPR database (Hirschmann et al. 2008 ) yielded the titanite solubility equation (adj. r 2 = 0.95): \(\left( {TiO_{2} } \right)^{melt} \left( {wt.\% } \right) = 0.978 \times M + 0.0048 \times T\left( K \right){-}5.90\) . This model correctly predicted undersaturation in 95% of 2344 experiments from the LEPR database that did not have titanite or rutile. Application to natural rocks yields saturation temperatures that are similar to independent temperature estimates. Finally, this equation should be useful for constraining the temperatures of titanite-saturated melts, for determining whether titanite saturation in magmatic source regions is likely, and for determining when titanite can crystallize and begin to exert an influence on melt geochemistry.
Gastrointestinal Dose-Histogram Effects in the Context of Dose-Volume–Constrained Prostate Radiation Therapy: Analysis of Data From the RADAR Prostate Radiation Therapy Trial
Purpose: To use a high-quality multicenter trial dataset to determine dose-volume effects for gastrointestinal (GI) toxicity following radiation therapy for prostate carcinoma. Influential dose-volume histogram regions were to be determined as functions of dose, anatomical location, toxicity, and clinical endpoint. Methods and Materials: Planning datasets for 754 participants in the TROG 03.04 RADAR trial were available, with Late Effects of Normal Tissues (LENT) Subjective, Objective, Management, and Analytic (SOMA) toxicity assessment to a median of 72 months. A rank sum method was used to define dose-volume cut-points as near-continuous functions of dose to 3 GI anatomical regions, together with a comprehensive assessment of significance. Univariate and multivariate ordinal regression was used to assess the importance of cut-points at each dose. Results: Dose ranges providing significant cut-points tended to be consistent with those showing significant univariate regression odds-ratios (representing the probability of a unitary increase in toxicity grade per percent relative volume). Ranges of significant cut-points for rectal bleeding validated previously published results. Separation of the lower GI anatomy into complete anorectum, rectum, and anal canal showed the impact of mid-low doses to the anal canal on urgency and tenesmus, completeness of evacuation and stool frequency, and mid-high doses to the anorectum on bleeding and stool frequency. Derived multivariate models emphasized the importance of the high-dose region of the anorectum and rectum for rectal bleeding and mid- to low-dose regions for diarrhea and urgency and tenesmus, and low-to-mid doses to the anal canal for stool frequency, diarrhea, evacuation, and bleeding. Conclusions: Results confirm anatomical dependence of specific GI toxicities. They provide an atlas summarizing dose-histogram effects and derived constraints as functions of anatomical region, dose, toxicity, and endpoint for informing future radiation therapy planning.
Space Shuttle Main Engine performance analysis
For a number of years, NASA has relied primarily upon periodically updated versions of Rocketdyne's power balance model (PBM) to provide space shuttle main engine (SSME) steady-state performance prediction. A recent computational study indicated that PBM predictions do not satisfy fundamental energy conservation principles. More recently, SSME test results provided by the Technology Test Bed (TTB) program have indicated significant discrepancies between PBM flow and temperature predictions and TTB observations. Results of these investigations have diminished confidence in the predictions provided by PBM, and motivated the development of new computational tools for supporting SSME performance analysis. A multivariate least squares regression algorithm was developed and implemented during this effort in order to efficiently characterize TTB data. This procedure, called the 'gains model,' was used to approximate the variation of SSME performance parameters such as flow rate, pressure, temperature, speed, and assorted hardware characteristics in terms of six assumed independent influences. These six influences were engine power level, mixture ratio, fuel inlet pressure and temperature, and oxidizer inlet pressure and temperature. A BFGS optimization algorithm provided the base procedure for determining regression coefficients for both linear and full quadratic approximations of parameter variation. Statistical information relative to data deviation from regression derived relations was also computed. A new strategy for integrating test data with theoretical performance prediction was also investigated. The current integration procedure employed by PBM treats test data as pristine and adjusts hardware characteristics in a heuristic manner to achieve engine balance. Within PBM, this integration procedure is called 'data reduction.' By contrast, the new data integration procedure, termed 'reconciliation,' uses mathematical optimization techniques, and requires both measurement and balance uncertainty estimates. The reconciler attempts to select operational parameters that minimize the difference between theoretical prediction and observation. Selected values are further constrained to fall within measurement uncertainty limits and to satisfy fundamental physical relations (mass conservation, energy conservation, pressure drop relations, etc.) within uncertainty estimates for all SSME subsystems. The parameter selection problem described above is a traditional nonlinear programming problem. The reconciler employs a mixed penalty method to determine optimum values of SSME operating parameters associated with this problem formulation.
Machine Learning for Mapping Multipactor Susceptibility in RF Systems: Capabilities and Generalization Constraints
Multipactor is a surface-driven electron avalanche phenomenon that degrades the performance and reliability of radio-frequency (RF) systems in particle accelerator and vacuum electronics applications. Multipactor behavior in a given device structure is conventionally assessed through susceptibility charts, which provide a parameter-space characterization of the instability. In this work, we assess the capabilities of machine-learning (ML) models to learn and predict such susceptibility charts and analyze the constraints governing their generalization across materials. Using a simulation-derived dataset spanning six distinct secondary-electron-yield material profiles in a canonical two-surface planar geometry, we train supervised regression models and artificial neural networks to predict the time-averaged electron growth rate, δavg, across the relevant parameter space. Model performance is evaluated using metrics that explicitly probe the structure of susceptibility charts, including Intersection over Union, Structural Similarity Index, and correlation analysis. Tree-based ensemble models outperform neural-network models in reconstructing susceptibility regions and in generalizing across material domains. Principal-component analysis reveals disjoint material feature distributions, indicating that the piecewise mode structure of multipactor susceptibility is difficult to represent with a single global model and that generalization is constrained by data coverage rather than by model complexity. An exhaustive reduced-coverage study further shows that sparse material-space coverage can yield mean performance in the same general range but producing large variability in the susceptibility-region overlap. These results clarify the capabilities of ML-based surrogate models for parameter-space characterization of multipactor discharge. They also provide guidance for their appropriate use in RF system design.
Wavelength Dependence of Activity-induced Photometric Variations for Young Cool Stars in Hyades
We investigate photometric variations due to stellar activity that induce systematic radial-velocity errors (so-called “jitter”) for the four targets in the Hyades open cluster observed by the K2 mission (EPIC 210721261, EPIC 210923016, EPIC 247122957, and EPIC 247783757). Applying Gaussian process regressions to the K2 light curves and the near-infrared (NIR) light curves observed with the IRSF 1.4 m telescope, we derive the wavelength dependences of the photometric signals due to stellar activity. To estimate the temporal variations in the photometric variability amplitudes between the two observation periods of K2 and IRSF, separated by more than 2 yr, we analyze a number of K2 targets in Hyades that have also been observed in Campaigns 4 and 13 and find a representative variation rate over 2 yr of 38% ± 71%. Taking this temporal variation into account, we constrain projected sizes and temperature contrast properties of the starspots in the stellar photosphere to be approximately 10% and 0.95%, respectively. These starspot properties can induce relatively large differences in the variability amplitude over different observational passbands, and we find that radial-velocity jitter may be more suppressed in the NIR than previously expected. Our result supports profits of ongoing exoplanet search projects that are attempting to detect or confirm young planets in open clusters via radial-velocity measurements in the NIR.
Diffusion Creep of Enstatite at High Pressures Under Hydrous Conditions: Diffusion Creep of Hydrous Enstatite
Mantle convection and large-scale plate motion depend critically on the nature of the lithosphere-asthenosphere boundary and thus on the viscosity structure of Earth's upper mantle, which is determined by the rheological properties of its constituent minerals. To constrain the flow behavior of orthopyroxene, the second most abundant constituent of the upper mantle, deformation experiments were carried out in triaxial compressive creep on fine-grained (~6 μm) samples of enstatite at high pressures (3.8–6.3 GPa) and high temperatures (1323–1573 K) using a deformation-DIA apparatus. Based on results from this study, the deformation behavior of enstatite is quantitatively presented in the form of a flow law that describes the dependence of deformation rate on differential stress, water fugacity, temperature, and pressure. Specifically, the creep rate depends approximately linearly on stress, indicating deformation in the diffusion creep regime. A least squares regression fit to our data yielded a flow law for diffusion creep with an activation energy of ~200 kJ/mol and an activation volume of ~14 × 10 -6 m 3 /mol. The magnitude of the water-weakening effect is similar to that for olivine with a water fugacity exponent of r ≈ 0.7. This strong dependence of viscosity on water fugacity (concentration) indicates that the viscosity of an orthopyroxene-bearing mantle varies from one geological setting to another, depending on the large-scale water distribution. Based on the rheology contrast between olivine and enstatite, we conclude that olivine is weaker than enstatite throughout most of the upper mantle except in some shallow regions in the diffusion creep regime.
Refining localtype primordial non-Gaussianity: Sharpened bϕ constraints through bias expansion
Local-type primordial non-Gaussianity (PNG), predicted by many nonminimal models of inflation, creates a scale-dependent contribution to the power spectrum of large-scale structure tracers. Its amplitude is characterized by the product bϕfNLloc, where bϕ is an astrophysical parameter dependent on the properties of the tracer. However, bϕ exhibits significant secondary dependence on halo concentration and other astrophysical properties, which may bias and weaken the constraints on fNLloc. In this work, we demonstrate that incorporating knowledge of the relation between Lagrangian bias parameters and bϕ can significantly enhance PNG constraints. We employ the hybrid effective field theory approach at the field level and a linear regression model to seek a connection between the bias parameters and bϕ for halo and galaxy samples, constructed using the abacussummit simulation suite and mimicking the luminous red galaxies and quasistellar objects of the Dark Energy Spectroscopic Instrument survey. For the fixed-mass halo samples, our full bias model reduces the uncertainty by more than 70%, with most of that improvement coming from b∇, which we find to be an excellent proxy for concentration. For the galaxy samples, our model reduces the uncertainty on bϕ by 80% for all tracers. By adopting Lagrangian-bias informed priors on the parameter bϕ, future analyses can thus constrain fNLloc with less bias and smaller errors.
Contrasting Carbon–Water–Energy Dynamics in Perennial and Annual Bioenergy Agroecosystems Using Eddy Covariance and Interpretable Machine Learning
Understanding how agroecosystems respond to environmental variability is fundamental to predicting productivity and sustainability under a changing climate. We analyzed 55 site-years of high-frequency eddy covariance observations from five agroecosystems—two perennial grasses (miscanthus and switchgrass), two annual rotation systems (maize–soybean and sorghum–soybean), and a restored native prairie—to examine ecosystem-scale carbon, water, and energy fluxes. Using an interpretable machine-learning framework with regression tree ensembles, Shapley Additive Explanations, and Accumulated Local Effects, we quantified how environmental and temporal factors regulate gross primary productivity (GPP), evapotranspiration (ET), water-use efficiency, and the Bowen ratio. Perennials exhibited stronger physiological buffering and maintained fluxes across a broader range of temperature and moisture conditions, reflecting deeper rooting and persistent canopy cover. Annuals, in contrast, showed greater short-term variability and stronger coupling to atmospheric demand, with GPP and ET declining rapidly under low humidity or soil moisture. Differences in temperature sensitivity of Bowen ratio further revealed that perennials sustained proportionally greater sensible heat flux under cool conditions, whereas annuals exhibited constrained energy exchange when evaporative demand was low. Together, these results demonstrate that crop life cycle and canopy structure are fundamental determinants of ecosystem-scale carbon–water–energy coupling. By integrating long-term flux observations with interpretable machine learning, this study identifies the environmental drivers that shape agroecosystem function and highlights how conversion from annual to perennial feedstocks can enhance climatic resilience and alter land–atmosphere energy feedbacks. These findings provide a data-driven basis for improving crop and Earth-system models and for guiding bioenergy landscape design under future climate scenarios.
The Weak Charge of the Proton. A Search For Physics Beyond the Standard Model
The Q weak experiment, which completed running in May of 2012 at Jefferson Laboratory, has measured the parity-violating asymmetry in elastic electron-proton scattering at four-momentum transfer Q 2 =0.025 (GeV/c) 2 in order to provide the first direct measurement of the proton's weak charge, Q W p . The Standard Model makes firm predictions for the weak charge; deviations from the predicted value would provide strong evidence of new physics beyond the Standard Model. Using an 89% polarized electron beam at 145 microA scattering from a 34.4 cm long liquid hydrogen target, scattered electrons were detected using an array of eight fused-silica detectors placed symmetric about the beam axis. The parity-violating asymmetry was then measured by reversing the helicity of the incoming electrons and measuring the normalized difference in rate seen in the detectors. The low Q 2 enables a theoretically clean measurement; the higher-order hadronic corrections are constrained using previous parity-violating electron scattering world data. The experimental method will be discussed, with recent results constituting 4% of our total data and projections of our proposed uncertainties on the full data set.