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Results for “Parameter Dimensionality Reduction”

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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

The two-loop five-point amplitude in $\mathcal{N}$ = 8 supergravity

We compute the symbol of the two-loop five-point amplitude in $\mathcal{N}$ = 8 supergravity. We write an ansatz for the amplitude whose rational prefactors are based on not only 4-dimensional leading singularities, but also d-dimensional ones, as the former are insufficient. Our novel d-dimensional unitarity-based approach to the systematic construction of an amplitude’s rational structures is likely to have broader applications, for example to analogous QCD calculations. We fix parameters in the ansatz by performing numerical integration-by-parts reduction of the known integrand. We find that the two-loop five-point $\mathcal{N}$ = 8 supergravity amplitude is uniformly transcendental. We then verify the soft and collinear limits of the amplitude. There is considerable similarity with the corresponding amplitude for $\mathcal{N}$ = 4 super-Yang-Mills theory: all the rational prefactors are double copies of the Yang-Mills ones and the transcendental functions overlap to a large degree. As a byproduct, we find new relations between color-ordered loop amplitudes in $\mathcal{N}$ = 4 super-Yang-Mills theory.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Editorial: Selective Controls on Microbial Energy Metabolisms: From the Microscale to the Macroscale

A mechanistic and predictive understanding of the genotypic and phenotypic controls on microbial element cycling remains a grand challenge in microbiology. While the composition and concentration of carbon sources, electron donors, and electron acceptors are known to influence microbial community structure by selecting for microbial sub-populations with distinct catabolic and respiratory pathways, selective inhibitors and trace nutrient availability modulate the activity of metabolic enzymes. This in turn influences the distribution of microbial sub-populations with distinct metabolic and respiratory traits, which are then structured by complex multi-dimensional environmental gradients that influence the composition, gene content and element cycling activity of microbiomes. While some parameters are known to select for different respiratory activities (e.g., lanthanides as essential nutrients for methanotrophs, molybdate as a specific inhibitor of sulfate reduction, carbon:nitrogen ratio and concentration as a control on the end-products of nitrate respiration), there are others to discover, and demonstrating how selective parameters operate and mediate element cycling across scales requires multi-disciplinary research in both the lab and field.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1997

Advanced Film Cooling Rig: Development and Test Results

The Advanced Film Cooling Rig (AFCR) test was a program to develop a thermal imaging camera system to measure the effectiveness of multihole film cooling. This system was used to evaluate low-thermal conductivity ceramic test panels incorporating a wide range of multihole cooling geometries. Tests were conducted with a mainstream flow of 600 F at Mach numbers of 0.37 and 1.57. Coolant flow was controlled to give a blowing parameter variation of up to 2.9. Data reduction techniques are given for camera calibration, rig temperature reflection correction, and deriving surface temperatures from video images. Results are presented in terms of overall and film cooling effectiveness distributions. A one-dimensional analysis of a unit film cooling hole was performed to generate film temperature.

Reilly, R. S.↗
NASA NTRS2008

Prediction of Turbulent Jet Mixing Noise Reduction by Water Injection

A one-dimensional control volume formulation is developed for the determination of jet mixing noise reduction due to water injection. The analysis starts from the conservation of mass, momentum and energy for the confrol volume, and introduces the concept of effective jet parameters (jet temperature, jet velocity and jet Mach number). It is shown that the water to jet mass flow rate ratio is an important parameter characterizing the jet noise reduction on account of gas-to-droplet momentum and heat transfer. Two independent dimensionless invariant groups are postulated, and provide the necessary relations for the droplet size and droplet Reynolds number. Results are presented illustrating the effect of mass flow rate ratio on the jet mixing noise reduction for a range of jet Mach number and jet Reynolds number. Predictions from the model show satisfactory comparison with available test data on perfectly expanded hot supersonic jets. The results suggest that significant noise reductions can be achieved at increased flow rate ratios.

Kandula, Max↗
NASA NTRS2007

Effective Jet Properties for the Prediction of Turbulent Mixing Noise Reduction by Water Injection

A one-dimensional control volume formulation is developed for the determination of jet mixing noise reduction due to water injection. The analysis starts from the conservation of mass, momentum and energy for the control volume, and introduces the concept of effective jet parameters (jet temperature, jet velocity and jet Mach number). It is shown that the water to jet mass flow rate ratio is an important parameter characterizing the jet noise reduction on account of gas-to-droplet momentum and heat transfer. Two independent dimensionless invariant groups are postulated, and provide the necessary relations for the droplet size and droplet Reynolds number. Results are presented illustrating the effect of mass flow rate ratio on the jet mixing noise reduction for a range of jet Mach number and jet Reynolds number. Predictions from the model show satisfactory comparison with available test data on supersonic jets. The results suggest that significant noise reductions can be achieved at increased flow rate ratios.

Kandula, Max↗
DOE OSTI2023

Dimensionality Reduction with Variational Encoders Based on Subsystem Purification

Efficient methods for encoding and compression are likely to pave the way toward the problem of efficient trainability on higher-dimensional Hilbert spaces, overcoming issues of barren plateaus. Here, we propose an alternative approach to variational autoencoders to reduce the dimensionality of states represented in higher dimensional Hilbert spaces. To this end, we build a variational algorithm-based autoencoder circuit that takes as input a dataset and optimizes the parameters of a Parameterized Quantum Circuit (PQC) ansatz to produce an output state that can be represented as a tensor product of two subsystems by minimizing $Tr(ρ^2)$. The output of this circuit is passed through a series of controlled swap gates and measurements to output a state with half the number of qubits while retaining the features of the starting state in the same spirit as any dimension-reduction technique used in classical algorithms. The output obtained is used for supervised learning to guarantee the working of the encoding procedure thus developed. We make use of the Bars and Stripes (BAS) dataset for an 8 × 8 grid to create efficient encoding states and report a classification accuracy of 95% on the same. Thus, the demonstrated example provides proof for the working of the method in reducing states represented in large Hilbert spaces while maintaining the features required for any further machine learning algorithm that follows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Iterative methods for dose reduction and image enhancement in tomography

A system and method for creating a three dimensional cross sectional image of an object by the reconstruction of its projections that have been iteratively refined through modification in object space and Fourier space is disclosed. The invention provides systems and methods for use with any tomographic imaging system that reconstructs an object from its projections. In one embodiment, the invention presents a method to eliminate interpolations present in conventional tomography. The method has been experimentally shown to provide higher resolution and improved image quality parameters over existing approaches. A primary benefit of the method is radiation dose reduction since the invention can produce an image of a desired quality with a fewer number projections than seen with conventional methods.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Gradient-Free Construction of Active Subspaces for Dimension Reduction in Complex Models with Applications to Neutronics

Recent developments in the field of reduced-order modeling---and, in particular, active subspace construction---have made it possible to efficiently approximate complex models by constructing low-order response surfaces based upon a small subspace of the original high-dimensional parameter space. These methods rely upon the fact that the response tends to vary more prominently in a few dominant directions defined by linear combinations of the original inputs, allowing for a rotation of the coordinate axis and a consequent transformation of the parameters. In this work, we discuss a gradient-free active subspace algorithm that is feasible for high-dimensional parameter spaces where finite-difference techniques are impractical. This analysis extends the gradient-free algorithm introduced in [A. Lewis, R. Smith, and B. Williams, Comput. Math. Appl., 72 (2016), pp. 1603--1615] in two significant ways: (i) we introduce an initialization algorithm to identify lower-dimensional subspaces of influential directions to seed the gradient-free algorithm for high-dimensional problems, and (ii) we analyze dimension selection criteria to verify the algorithms. Finally, we illustrate the initialized gradient-free active subspace algorithm for a neutronics example implemented with SCALE6.1 for input dimensions up to 7700.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1996

Flap Side-Edge Noise: Acoustic Analysis of Sen's Model

The two-dimensional flap side-edge flow model developed by Sen is analyzed to reveal the noise production potential of the proposed mechanism. The model assumes that a vortex will form at the equilibrium position off the side edge of the flap. The vortex is then perturbed away from the equilibrium position by incoming turbulence causing it to oscillate and thus radiate sound. The noise field is calculated three-dimensionally by taking the flap to have a finite chord. Spectra and directivity of the farfield sound are presented. In addition, the effect of retarded time differences is evaluated. The parameters in the model are related to typical aircraft parameters and noise reduction possibilities are proposed.

Hardin, Jay C.↗
DOE OSTI2025

Compressing Vision Transformers in Geospatial Transfer Learning with Manifold-Constrained Optimization

Deploying geospatial foundation models on resource-constrained edge devices demands compact architectures that maintain high downstream performance. However, their large parameter counts and the accuracy loss often induced by compression limit practical adoption.In this work, we leverage manifold-constrained optimization framework DLRT to compress large vision transformer–based geospatial foundation models during transfer learning. By enforcing structured low-dimensional parameterizations aligned with downstream objectives, this approach achieves strong compression while preserving task-specific accuracy. We show that the method outperforms of-the-shelf low-rank methods as LoRA. Experiments on diverse geospatial benchmarks confirm substantial parameter reduction with minimal accuracy loss, enabling high-performing, on-device geospatial models.

Snyder, Thomas [Yale University]↗
DOE OSTI2026

DP-TwoLevel: two-stage gradient subspace learning for differentially private federated learning

Federated learning (FL) enables collaborative model training across distributed data sources without sharing raw data, but faces fundamental challenges in communication efficiency and privacy. Differentially private (DP) training mitigates information leakage but introduces noise that degrades model performance, especially in high-dimensional settings. We propose DP-TwoLevel, a hierarchical gradient projection method that improves utility under fixed DP constraints by exploiting low-dimensional structure in model updates. Our approach learns a two-level PCA-based representation of gradients and applies DP noise in a reduced-dimensional subspace, thereby lowering the effective noise magnitude while preserving dominant signal components. We evaluate the method across three datasets (MNIST, Fashion-MNIST, CIFAR-10) and three privacy regimes (ϵ∈0.5, 1.0, 2.0). Across nine experimental settings, DP-TwoLevel consistently outperforms DP-FedAvg, achieving an average accuracy improvement of 9.44%, with larger gains observed in lower ϵ(higher-noise) regimes (up to +22.31%). We further analyze scalability across models ranging from 100K to 1.49M parameters and identify a variance-based success criterion: performance remains strong when the projection preserves more than 75% of gradient variance, degrades in a marginal regime (65–75%), and fails below this threshold. Our results demonstrate that structure-aware dimensionality reduction can significantly improve the privacy–utility tradeoff in FL without modifying formal privacy guarantees. We also provide empirical evidence of scaling limitations for global projections and motivate per-layer extensions for larger models.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗
NASA NTRS2024

On the Validity of the Tada Stress Intensity Factor Solution for the Single Edge Notch Tension Specimen With Pinned Ends

The validity of the Tada stress intensity factor (K Tada ) for pinned-ends single edge notch tension [SEN(T)] specimens is assessed via a combined experimental-modeling approach. Analysis of fatigue crack growth rate reductions during constant-∆K Tada loading demonstrates that specific combinations of alloy stiffness, geometry, and loading result in the true K deviating below K Tada . Geometrically non-linear, 3-dimensional finite element calculations confirm mild-to-strong influences of these parameters, which are not captured by K Tada . Most existing pinned SEN(T) data are found to use parameters where KTada is not significantly reduced, but the present results underscore the need for a more broadly applicable K solution.

Non-linear FEA↗
DOE OSTI2015

Rholography, black holes and Scherk-Schwarz

We present a construction of a class of near-extremal asymptotically flat black hole solutions in four (or five) dimensional gauged supergravity with R-symmetry gaugings obtained from Scherk-Schwarz reductions on a circle. The entropy of these black holes is counted holographically by the well known MSW (or D1/D5) system, with certain twisted boundary conditions labeled by a twist parameter ρ. Here, we find that the corresponding (0, 4) (or (4, 4)) superconformal algebras are exactly those studied by Schwimmer and Seiberg, using a twist on the outer automorphism group. The interplay between R-symmetries, ρ-algebras and holography leads us to name our construction “Rholography”.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Lattice QED in an external magnetic field: Evidence for dynamical chiral symmetry breaking

We simulate quantum electrodynamics (QED) in a strong constant homogeneous external magnetic field on a Euclidean space-time lattice using the rational hybrid Monte Carlo method, developed for simulating lattice quantum chromodynamics (QCD). Our primary goal is to measure the chiral condensate in the limit when the input electron mass m is zero. We observe a nonzero value, indicating that the external magnetic field catalyzes chiral symmetry breaking as predicted by approximate truncated Schwinger-Dyson methods. Such behavior is associated with dominance by the lowest Landau level which causes the effective dimensional reduction from 3 + 1 dimensions to 1 + 1 dimensions for charged particles (electrons and positrons) where the attractive forces of QED can produce chiral symmetry breaking with a dynamical electron mass and associated chiral condensate. Since our lattice simulations use bare (lattice) parameters, while the Schwinger-Dyson analyses work with renormalized quantities, direct numerical comparison will require renormalization of our lattice results. Published by the American Physical Society 2024

Kogut, J. B. (ORCID:000000030151548X)↗
DOE OSTI2019

Function Preserving Projection

With function preserving projection (FPP), we aim to address high-dimensional data exploration challenges common in many scientific applications. Instead of preserving the global or local geometric structure of a dataset like traditional dimensionality reduction methods (e.g., principal component analysis, t-stochastic neighborhood embedding), the proposed approach finds a linear projection that reveal interpretable (yet potentially nonlinear) pattern of the properties of interests defined on a highdimensional domain (e.g., simulation yield defined on the input parameter space). The key insight driving FPP is the fact that while non-linear embeddings are difficult to interpret, humans are highly skilled in understanding complex visual patterns. FPP lets users select the complexity of a pattern they consider interpretable, allows to find a joint projection that explains multiple functions, and provides a confidence statistic (akin to a p-value) indicating how salient the resulting pattern is. We demonstrate the efficacy of the proposed method on several synthetic and scientific datasets for both continuous (regression) and discrete (classification) problems of interests and illustrate the scalability of FPP by finding an optimal projection for close to 40 gigabytes in around 10 minutes.

Liu, SHUSEN↗
DOE OSTI2024

Domain-decomposition nonlinear manifold reduced order model

This software combines nonlinear-manifold reduced order models (NM-ROMs) with domain decomposition (DD) techniques. NM-ROMs, which utilize a shallow, sparse autoencoder trained with full order model (FOM) snapshot data, approximate the FOM state on a nonlinear manifold. These models offer advantages over linear-subspace ROMs (LS-ROMs) particularly in scenarios with slowly decaying Kolmogorov n-width. However, the training of NM-ROMs involves a number of parameters that scale with the size of the FOM, and storing high-dimensional FOM snapshots can significantly increase the cost of ROM training for extreme-scale problems. To mitigate these costs, the software employs DD to partition the FOM into smaller subdomains, computes NM-ROMs for each, and then integrates these to form a global NM-ROM. This strategy offers multiple benefits: it enables parallel training of subdomain NM-ROMs, reduces the number of parameters needed, decreases the dimensional requirements of subdomain FOM training data, and allows for customization to the unique characteristics of each FOM subdomain. The use of a shallow, sparse autoencoder architecture in each subdomain NM-ROM facilitates the application of hyper-reduction (HR), simplifying the nonlinear complexities and enhancing computational speed. This software marks the inaugural application of NM-ROM combined with HR to a DD problem. It features an algebraic DD reformulation of the FOM, training of NM-ROMs with HR for each subdomain, and employs a sequential quadratic programming (SQP) solver for the evaluation of the coupled global NMROM. The effectiveness of the DD NM-ROM with HR is numerically demonstrated on the 2D steady-state Burgers' equation, showing an order of magnitude improvement in accuracy over the DD LS-ROM with HR.

Diaz, AlejandroN↗
DOE OSTI2025

Generative learning for slow manifolds and bifurcation diagrams

In dynamical systems characterized by separation of time scales, the approximation of so called “slow manifolds”, on which the long term dynamics lie, is a useful step for model reduction. Initializing on such slow manifolds is a useful step in modeling, since it circumvents fast transients, and is crucial in multiscale algorithms (like the equation-free approach) alternating between fine scale (fast) and coarser scale (slow) simulations. In a similar spirit, when one studies the infinite time dynamics of systems depending on parameters, the system attractors (e.g., its steady states) lie on bifurcation diagrams (curves for one-parameter continuation, and more generally, on manifolds in state parameter space. Sampling these manifolds gives us representative attractors (here, steady states of ODEs or PDEs) at different parameter values. Algorithms for the systematic construction of these manifolds (slow manifolds, bifurcation diagrams) are required parts of the “traditional” numerical nonlinear dynamics toolkit. In more recent years, as the field of Machine Learning develops, conditional score-based generative models (cSGMs) have been demonstrated to exhibit remarkable capabilities in generating plausible data from target distributions that are conditioned on some given label. It is tempting to exploit such generative models to produce samples of data distributions (points on a slow manifold, steady states on a bifurcation surface) conditioned on (consistent with) some quantity of interest (QoI, observable). In this work, we present a framework for using cSGMs to quickly (a) initialize on a low-dimensional (reduced-order) slow manifold of a multi-time-scale system consistent with desired value(s) of a QoI (a “label”) on the manifold, and (b) approximate steady states in a bifurcation diagram consistent with a (new, out-of-sample) parameter value. This conditional sampling can help uncover the geometry of the reduced slow-manifold and/or approximately “fill in” missing segments of steady states in a bifurcation diagram. Finally, the quantity of interest, which determines how the sampling is conditioned, is either known a priori or identified using manifold learning-based dimensionality reduction techniques applied to the training data.

Dynamical systems↗
DOE OSTI2016

Simulating bioclogging effects on dynamic riverbed permeability and infiltration

Bioclogging in rivers can detrimentally impact aquifer recharge. This is particularly so in dry regions, where losing rivers are common, and where disconnection between surface water and groundwater (leading to the development of an unsaturated zone) can occur. Reduction in riverbed permeability due to biomass growth is a time-variable parameter that is often neglected, yet permeability reduction from bioclogging can introduce order of magnitude changes in seepage fluxes from rivers over short (i.e., monthly) timescales. To address the combined effects of bioclogging and disconnection on infiltration, we developed in this paper numerical representations of bioclogging processes within a one-dimensional, variably saturated flow model representing losing-connected and losing-disconnected rivers. We tested these formulations using a synthetic case study informed with biological data obtained from the Russian River, California, USA. Our findings show that modeled biomass growth reduced seepage for losing-connected and losing-disconnected rivers. However, for rivers undergoing disconnection, infiltration declines occurred only after the system was fully disconnected. Before full disconnection, biologically induced permeability declines were not significant enough to offset the infiltration gains introduced by disconnection. The two effects combine to lead to a characteristic infiltration curve where peak infiltration magnitude and timing is controlled by permeability declines relative to hydraulic gradient gains. Biomass growth was found to hasten the onset of full disconnection; a condition we term ‘effective disconnection’. Finally, our results show that river infiltration can respond dynamically to bioclogging and subsequent permeability declines that are highly dependent on river connection status.

58 GEOSCIENCES↗