Search NASA⌕ Search

SEARCH · Search NASA

Results for “Regularization”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Inputs from regularly and irregularly discharging vestibular nerve afferents to secondary neurons in squirrel monkey vestibular nuclei. III. Correlation with vestibulospinal and vestibuloocular output pathways

1. A previous study measured the relative contributions made by regularly and irregularly discharging afferents to the monosynaptic vestibular nerve (Vi) input of individual secondary neurons located in and around the superior vestibular nucleus of barbiturate-anesthetized squirrel monkeys. Here, the analysis is extended to more caudal regions of the vestibular nuclei, which are a major source of both vestibuloocular and vestibulospinal pathways. As in the previous study, antidromic stimulation techniques are used to classify secondary neurons as oculomotor or spinal projecting. In addition, spinal-projecting neurons are distinguished by their descending pathways, their termination levels in the spinal cord, and their collateral projections to the IIIrd nucleus. 2. Monosynaptic excitatory postsynaptic potentials (EPSPs) were recorded intracellularly from secondary neurons as shocks of increasing strength were applied to Vi. Shocks were normalized in terms of the threshold (T) required to evoke field potentials in the vestibular nuclei. As shown previously, the relative contribution of irregular afferents to the total monosynaptic Vi input of each secondary neuron can be expressed as a %I index, the ratio (x100) of the relative sizes of the EPSPs evoked by shocks of 4 x T and 16 x T. 3. Antidromic stimulation was used to type secondary neurons as 1) medial vestibulospinal tract (MVST) cells projecting to spinal segments C1 or C6; 2) lateral vestibulospinal tract (LVST) cells projecting to C1, C6; or L1; 3) vestibulooculo-collic (VOC) cells projecting both to the IIIrd nucleus and by way of the MVST to C1 or C6; and 4) vestibuloocular (VOR) neurons projecting to the IIIrd nucleus but not to the spinal cord. Most of the neurons were located in the lateral vestibular nucleus (LV), including its dorsal (dLV) and ventral (vLV) divisions, and adjacent parts of the medial (MV) and descending nuclei (DV). Cells receiving quite different proportions of their direct inputs from regular and irregular afferents were intermingled in all regions explored. 4. LVST neurons are restricted to LV and DV and show a somatotopic organization. Those destined for the cervical and thoracic cord come from vLV, from a transition zone between vLV and DV, and to a lesser extent from dLV. Lumbar-projecting neurons are located more dorsally in dLV and more caudally in DV. MVST neurons reside in MV and in the vLV-DV transition zone.(ABSTRACT TRUNCATED AT 400 WORDS).

NASA Discipline Neuroscience↗
NASA NTRS2010

Modeling Regular Replacement for String Constraint Solving

Bugs in user input sanitation of software systems often lead to vulnerabilities. Among them many are caused by improper use of regular replacement. This paper presents a precise modeling of various semantics of regular substitution, such as the declarative, finite, greedy, and reluctant, using finite state transducers (FST). By projecting an FST to its input/output tapes, we are able to solve atomic string constraints, which can be applied to both the forward and backward image computation in model checking and symbolic execution of text processing programs. We report several interesting discoveries, e.g., certain fragments of the general problem can be handled using less expressive deterministic FST. A compact representation of FST is implemented in SUSHI, a string constraint solver. It is applied to detecting vulnerabilities in web applications

Fu, Xiang↗
NASA NTRS2017

Products of the SNPP VIIRS SD Screen Transmittance and the SD BRDFs From Both Yaw Maneuver and Regular On-Orbit Data

To ensure data quality, the Earth-observing Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership satellite regularly performs on-orbit radiometric calibration of its 22 spectral bands. The primary radiance source for the calibration of the VIIRS reflective solar bands (RSBs) is a sunlit onboard solar diffuser (SD).During the calibration process, sunlight goes through a perforated plate (the SD screen) and then strikes the SD. The sunlight, scattered off the SD of near-Lambertian property, is used for the calibration. Consequently, the spectral radiance of the scattered sunlight is proportional to the product of the SD screen transmittance and the SD bidirectional reflectance distribution function (BRDF) value at the observation direction. The BRDF value is decomposed to the product of its initial value at launch and a numerical degradation factor that quantifies the decrease from the initial value. The degradation factor is determined by an onboard SD stability monitor (SDSM). During the BRDF degradation factor determination process, the SDSM receives the SD scattered sunlight and the sunlight that goes through another perforated plate at almost the same time. The ratio of the signal strengths from the two observations is used to determine the BRDF degradation factor. Consequently, the RSB radiometric calibration requires the accurate knowledge of the product of the SD screen transmittance and the initial BRDF value as sensed by the RSB and the SDSM detectors. We use both yaw maneuver and a small portion of regular on-orbit data to determine the products.

Lei, Ning↗
NASA NTRS2018

Initial Determination of the NOAA-20 VIIRS Screen Transmittances with Both Yaw Maneuver and Regular on Orbit Data

One of the scientific instruments aboard the NOAA-20 satellite is the Visible Infrared Imaging Radiometer Suite (VIIRS). The VIIRS regularly performs on-orbit radiometric calibration of its reflective solar bands, primarily through observations of an onboard sunlit solar diffuser (SD). The incident sunlight passes through an attenuation screen (the SD screen) and scatters off the SD to provide a radiance source for the calibration. The on-orbit change of the SD bidirectional reflectance distribution function (BRDF), denoted as the H-factor, is determined by an onboard solar diffuser stability monitor (SDSM). The eight SDSM detectors observe the sun through another attenuation screen (the SDSM screen) and the sunlit SD almost at the same time to measures the SD BRDF change. The products of the SD screen transmittance and the BRDF at t=0 and the SDSM screen transmittance were measured prelaunch. Large undulations in the H-factor were seen when using the prelaunch screen transmittances. Fifteen on-orbit yaw maneuvers were performed to validate and to further characterize the screens. Although significantly improved, the H-factor from the yaw maneuver data determined screen transmittance still has undulations as large as about 0.7-0.8%, revealing that the angular step size of the yaw maneuvers is too large. In this paper, we add regular on-orbit data to the yaw maneuver data to further improve the relative products and the relative SDSM screen transmittance. The H-factor time series derived from the newly determined screen transmittance is much smoother than that derived from using only the yaw maneuver data and thus improves considerably the radiometric calibration accuracy.

Lei, Ning↗
NASA NTRS2017

Optimizing the Regularization in Broadband Wavefront Control Algorithm for WFIRST Coronagraph

The WFIRST/AFTA 2.4 m space telescope currently under study includes a stellar coronagraph for the imaging and the spectral characterization of extrasolar planets. The coronagraph employs sequential deformable mirrors to compensate for phase and amplitude errors. Using the optical model of an Occulting Mask Coronagraph (OMC) testbed at the Jet Propulsion Laboratory (JPL), we have investigated and compared through modeling and simulations the performance of several actuator regularization-schemes in broadband wavefront control algorithm used to generate dark holes in an OMC, such as a Hybrid Lyot Coronagraph (HLC). Using the concept of a Tikhonov filter constituting the G-matrix, we have explained what the different regularization schemes do to singular-modes during a wavefront control process called Electric Field Conjugation (EFC). In some cases we confirmed the numerical predictions with the testbed measured results. We present our findings in this paper.

Sidick, Erkin↗
NASA NTRS2020

Determination of the NOAA-20 VIIRS screen transmittance functions with both the yaw maneuver and regular on-orbit calibration data

The Visible Infrared Imaging Radiometer Suite (VIIRS) aboard theNOAA-20 satellite regularly performs on-orbit radiometric calibration of its reflective solar bands (RSBs) through observations of an onboard sunlit solar diffuser (SD). The incident sunlight passes through an attenuation screen (the SD screen) and then scatters off the SD to provide a radiance source for the calibration. The on-orbit change of the SD’s bidirectional reflectance distribution function (BRDF), referred to as the H-factor, is determined by an onboard solar diffuser stability monitor (SDSM) whose eight detectors alternately observe the Sun through another attenuation screen (the SDSM screen) and the sunlit SD. The products of the SD screen transmittance and the BRDF at the mission start for both the SDSM and RSBs and the SDSM screen effective transmittance were measured prelaunch. Large unrealistic undulations in the retrieved H-factor were seen when using the prelaunch screen functions. To improve the accuracy of the retrieved H-factor, shortly after the satellite launch, 15 yaw maneuvers were performed to further characterize the screens. Although significantly improved, the H-factor derived using the screen functions determined fromthe yaw maneuver data still has large unrealistic undulations, revealing that the solar azimuth angular step size of the yaw maneuvers is too large. In this paper,we add high-quality regular on-orbitSDcalibration data to the yaw maneuver data to further improve the relative product of the SD screen effective transmittance and the BRDF at the mission start for the SDSM and the SDSM screen relative effective transmittance. The H-factor time series derived from the newly determined screen transmittance functions is much smoother than that derived from using only the yaw maneuver data and thus considerably improves the radiometric calibration accuracy.

Ning Lei↗
DOE OSTI2025

Dynamic Low-Rank Training with Spectral Regularization: Achieving Robustness in Compressed Representations

Deployment of neural networks on resource-constrained devices demands models that are both compact and robust to adversarial inputs. However, compression and adversarial robustness often conflict. In this work, we introduce a dynamical low-rank training scheme enhanced with a novel spectral regularizer that controls the condition number of the low-rank core in each layer. This approach mitigates the sensitivity of compressed models to adversarial perturbations without sacrificing clean accuracy. The method is model- and data-agnostic, computationally efficient, and supports rank adaptivity to automatically compress the network at hand. Extensive experiments across standard architectures, datasets, and adversarial attacks show the regularized networks can achieve over 94\% compression while recovering or improving adversarial accuracy relative to uncompressed baselines.

Schotthoefer, Steffen [ORNL] (ORCID:00000002156965↗
DOE OSTI2026

Adaptive spectra-to-exposure conversion using ridge regularized polynomial response models

Real-time gamma spectra-to-exposure conversion in aerial and ground monitoring commonly relies on calibration-derived, detector- or system-specific conversion coefficients that are assumed to generalize across operational environments. In practice, deployment specific differences in spectral composition and transport conditions can introduce systematic bias relative to reference instruments, motivating methods that adapt coefficients using minimal field supervision while explicitly limiting overfitting. In this work, we present a conservative coefficient adaptation framework that updates a baseline polynomial energy-weighting function using ridge-regularized regression, with leave-one-out cross-validation (LOOCV) used to select the regularization strength. The findings support ridge-constrained minimal-supervision adaptation as a practical mechanism to suppress site-specific bias without destabilizing a calibration-derived baseline.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

A kinetic-based regularization method for data science applications

We propose a physics-based regularization technique for function learning, inspired by statistical mechanics. By drawing an analogy between optimizing the parameters of an interpolator and minimizing the energy of a system, we introduce corrections that impose constraints on the lower-order moments of the data distribution. This minimizes the discrepancy between the discrete and continuum representations of the data, in turn allowing to access more favorable energy landscapes, thus improving the accuracy of the interpolator. Our approach improves performance in both interpolation and regression tasks, even in high-dimensional spaces. Unlike traditional methods, it does not require empirical parameter tuning, making it particularly effective for handling noisy data. We also show that thanks to its local nature, the method offers computational and memory efficiency advantages over Radial Basis Function interpolators, especially for large datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Automated shaker placement and regularized input estimation for MIMO testing.

Multi-input, multi-output (MIMO) testing is used in component qualification to reproduce operational responses in the laboratory. It is often preferred to single-input and base-shake testing because of the potential for equivalent or better tests using smaller actuators and shorter test suites. Given a target response, two key steps in MIMO test design are selecting actuator locations and solving for input loads. Actuator locations are often manually selected using expert judgment. If an automatic method is used, locations are usually determined by simulating the vibration control problem and minimizing a combination of the input energy and control residuals. To select a configuration, the relative importance of input energy and residuals must be specified. Specifying relative weights is, in general, a manual and subjective process. This paper develops an objective function that compares actuator configurations based on control accuracy and required input energy without any manual parameter tuning. The objective function uses an optimally selected tradeoff parameter for each candidate configuration. To choose actuator locations using the new objective function, a pivoting algorithm for integer programming problems is developed. Starting with an initial configuration (such as the one generated by a greedy algorithm), the pivoting algorithm guarantees an objective function decrease in each iteration until convergence is reached. In a simulation featuring a structure excited by a diffuse acoustic field, electrodynamic shaker locations and regularized inputs are solved for without any analyst-specified parameters. Simulations are performed in MIMO configurations where the number of target responses is less than, equal to, and greater than the number of actuators.

Multi-input multi-output↗
DOE OSTI2024

From irregular to regular eutectic growth in the Al-Al 3 Ni system: In situ observations during directional solidification

Here we investigate the irregular eutectic growth dynamics of the Al-Al 3 Ni alloy, in which one of the solid phases (Al 3 Ni) grows faceted from the liquid. Leveraging in situ optical microscopy and synchrotron transmission x-ray microscopy, we address the question of the degree of coupling between Al and Al 3 Ni at the growth front and that of the shape of the microstructures left behind in the bulk solid during directional solidification. Real-time optical observations bring evidence for a morphological transition from a eutectic-grain dependent, irregular eutectic growth at low solidification velocity V (typically 1 μm s -1 ), to a weakly anisotropic, regular growth at higher V ( reaching 10 μm s -1 ). Unprecedented x-ray nano-imaging of the solid–liquid interface, and 3D characterization of the growth patterns, were made possible by a new DS setup at Brookhaven National Laboratory’s NSLS-II. At low V, the leading tips of partly faceted Al 3 Ni crystals are observed to grow not far ahead of the Al growth front. Correlating in situ images and postmortem 3D tomographic reconstructions reveals that the presence of faceted and non-faceted regions of Al 3 Ni crystals in the solid is a direct consequence of coupling and decoupling during DS, respectively. Upon increasing V, the lead distance of Al 3 Ni vanishes, and the shape of Al 3 Ni ceases to be governed by faceted growth. These observations cast light onto the basic mechanisms (faceted growth, diffusive coupling, and the dynamics of trijunctions) governing a faceted to rod-like transition upon increasing V in the Al 3 Ni system, with broad implications to a large class of irregular eutectics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

PINN surrogate of Li-ion battery models for parameter inference, Part II: Regularization and application of the pseudo-2D model

Bayesian parameter inference is useful to improve Li-ion battery diagnostics and can help formulate battery aging models. However, it is computationally intensive and cannot be easily repeated for multiple cycles, multiple operating conditions, or multiple replicate cells. To reduce the computational cost of Bayesian calibration, numerical solvers for physics-based models can be replaced with faster surrogates. A physics-informed neural network (PINN) is developed as a surrogate for the pseudo-2D (P2D) battery model calibration. For the P2D surrogate, additional training regularization was needed as compared to the PINN single-particle model (SPM) developed in Part I. Both the PINN SPM and P2D surrogate models are exercised for parameter inference and compared to data obtained from a direct numerical solution of the governing equations. A parameter inference study highlights the ability to use these PINNs to calibrate scaling parameters for the cathode Li diffusion and the anode exchange current density. By realizing computational speed-ups of ~2250x for the P2D model, as compared to using standard integrating methods, the PINN surrogates enable rapid state-of-health diagnostics. Finally, in the low-data availability scenario, the testing error was estimated to ~2 mV for the SPM surrogate and ~10 mV for the P2D surrogate which could be mitigated with additional data.

25 ENERGY STORAGE↗
DOE OSTI2024

A regularized-interface method as a unified formulation for simulations of high-pressure multiphase flows

The injection of multi-species fluids into high-pressure and high-temperature environments beyond the species' critical points is commonly found in engineering applications. At these conditions, for immiscible species, both subcritical interfacial dynamics and supercritical mixing can coexist due to variations in temperature around the mixture critical point. The modeling of these complex transcritical phenomena for large-scale configurations is so far not possible. To address this issue, we propose the Regularized-Interface Method (RIM) as a unified formulation that can describe both sub- and supercritical processes as well as the transition between them. The proposed method is derived via filtering of the nanoscale interface-resolving formulation based on van der Waals' linear gradient theory. Thus, this approach allows for the consistent modeling of interfacial dynamics that vanishes at supercritical conditions, while significantly reducing the temporal and spatial resolution constraints of the original nanoscale formulation. The resulting RIM formulation is examined in interface-capturing simulations of sub-, trans-, and supercritical fuel injection processes, involving droplets and jets. Furthermore, these results highlight the importance of resolving spatio-temporal transitions from subcritical interfacial dynamics to supercritical mixing in high-pressure multiphase simulations, in contrast to commonly employed diffused-interface methods, where interfacial dynamics are often neglected.

Interface capturing↗
DOE OSTI2025

Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography

Obtaining high-quality 3D reconstructions from electron tomography of crystalline particles embedded in lighter support elements is crucial for various material systems such as catalysts for fuel cell applications. However, significant challenges arise due to the limited tilt range, sparse and low signal-to-noise ratio of the measurements. In addition, small metal particles can cause strong streaking and shading artifacts in the 3D reconstructions when using conventional reconstruction algorithms due to the presence of Bragg diffraction and the large scattering cross-section difference between the materials of the particles and the background support regions. These artifacts lead to errors in the downstream characterization affecting extraction of critical features such as the size of the metal particles, their distribution and the volume of the lighter support regions. In this paper, we present a two-stage algorithm based on metal artifact reduction, utilizing model-based iterative reconstruction methods with adaptive adjustment of regularization parameters. Our approach yields high-quality 3D reconstructions compared to traditional algorithms, accurately capturing both the metal particles as well as the background support. We demonstrate the effectiveness of our algorithm through simulated and experimental bright-field electron tomography data, showing significant improvements in reconstruction quality compared to traditional methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Online regularization of Poincaré map of storage rings with Shannon entropy

A measurable chaos indicator is used as the online optimization objective in tuning a complicated nonlinear system—the National Synchrotron Light Source-II storage ring. Through analyzing the Shannon entropy in measured Poincaré maps, not only can the commonly used nonlinear characterizations be extracted, but more importantly, the chaos can be quantified and then used for an online regularization of these maps. The method itself is general and applicable to other tunable nonlinear systems as well. Published by the American Physical Society 2025

36 MATERIALS SCIENCE↗
DOE OSTI2025

Phases of 2D massless QCD with qubit regularization

We investigate the possibility of reproducing the continuum physics of 2D S U ( N ) gauge theory coupled to a single flavor of massless Dirac fermion using qubit regularization. The continuum theory is described by N free fermions in the ultraviolet (UV) and a coset Wess-Zumino-Witten (WZW) model in the infrared (IR). In this work, we first explore how well these features can be reproduced using the Kogut-Susskind (KS) Hamiltonian with a finite-dimensional link Hilbert space and a generalized Hubbard coupling. We do this by analyzing the renormalization group (RG) flow diagram of the continuum theory and identifying important phases of the theory. Using strong coupling expansions, we show that our lattice model exhibits a gapped dimer phase and a spin-chain phase. Furthermore, for N = 2 , using tensor network methods, we show that there is a second-order phase transition between these two phases, which we identify as the critical surface of the continuum theory that connects the IR and UV fixed points. In the IR, we identify the critical theory at the transition as the expected S U ( 2 ) 1 WZW model. Lastly, we argue that modifications of our model may allow the study of the UV physics of free fermions. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Regularizing INR with Diffusion Prior for Self-Supervised 3D Reconstruction OF Neutron Computed Tomography Data

Recently, generative diffusion priors have made huge strides as inverse problem solvers, including the ability to be adapted for inference on out-of-distribution data. Concurrently, implicit neural representations (INRs) have emerged as fast and lightweight inverse imaging solvers that are amenable to hybrid approaches that combine learned priors with traditional inverse problem formulations. In this paper, we present a diffusive computed tomography (CT) inversion framework for regularizing INRs called Diffusive INR (DINR), designed to enable high-quality reconstruction from sparse-view neutron CT. Pretrained purely on synthetic data, DINR is evaluated on simulated and experimentally obtained observations of concrete microstructures, where traditional reconstruction methods suffer substantial degradation when the number of views is reduced. Our approach delivers superior performance, reduces reconstruction artifacts, and achieves gains in PSNR and SSIM, enabling accurate micro-structural characterization even under extreme data limitations compared to state-of-the-art sparse-view reconstruction techniques.

Hossain, Maliha [ORNL]↗
DOE OSTI2026

REV-INR: Regularized Evidential Implicit Neural Representation for Uncertainty-Aware Volume Visualization

Applications of Implicit Neural Representations (INRs) have emerged as a promising deep learning approach for compactly representing large volumetric datasets. These models can act as surrogates for volume data, enabling efficient storage and on-demand reconstruction via model predictions. However, conventional deterministic INRs only provide value predictions without insights into the model’s prediction uncertainty or the impact of inherent noisiness in the data. This limitation can lead to unreliable data interpretation and visualization due to prediction inaccuracies in the reconstructed volume. Identifying erroneous results extracted from model-predicted data may be infeasible, as raw data may be unavailable due to its large size. To address this challenge, we introduce REV-INR, Regularized Evidential Implicit Neural Representation, which learns to predict data values accurately along with the associated coordinate-level data uncertainty and model uncertainty using only a single forward pass of the trained REV-INR during inference. By comprehensively comparing and contrasting REV-INR with existing well-established deep uncertainty estimation methods, we show that REV-INR achieves the best volume reconstruction quality with robust data (aleatoric) and model (epistemic) uncertainty estimates using the fastest inference time. Consequently, we demonstrate that REV-INR facilitates assessment of the reliability and trustworthiness of the extracted isosurfaces and volume visualization results, enabling analyses to be solely driven by model-predicted data.

Saklani, Shanu [Indian Institute of Technology, Ka↗