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At least 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Extension of the PINN diffusion model to k-eigenvalue problems

This paper extends our recent work on the Physics-Informed Neural Networks (PINN) approach for the fixed source diffusion models and applies it to the diffusion theory based k-eigenvalue problems. To make the PINN equitable for the eigenvalue problems, we introduce a novel integral regularization term to the loss function in the framework, and allow the direct inference of the principal eigenvalue and the associated eigenfunction. The regularization term enforces a pre-defined value on the integration of the model predictions, and this value can be directly related to a physical property of the system. We also introduce an additional learnable parameter to approximate the principal eigenvalue. As a proof of principle, we solve the one-group two-dimensional k-eigenvalue neutron diffusion equation in this work. We then provide two numerical examples to demonstrate the applicability of the PINN approach. In each example, we solve the k-eigenvalue diffusion equation in a multi-region configuration constrained with a set of Robin boundary conditions for generality. We use a FEM solution based on the power-iteration method to verify the results of the PINN solution. The results showed relative percentage error in the predicted eigenvalue of about 0.77% and about 1.2% for example 1 and example 2, respectively. The mean absolute error in the predicted flux for example 1 is ∼ 0.002 and for example 2 is ∼ 0.0024. These results indicate some preliminary successes of the PINN application to k-eigenvalue problems. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Safe Exploration Reinforcement Learning for Load Restoration using Invalid Action Masking

This paper addresses the load restoration problem after a power outage event. Our primary proposed methodology uses a multi-agent reinforcement learning method to make the optimal sequential decisions on picking up critical loads. Typically, a negative reward is provided to discourage the agents from selecting decisions that violate physical constraints during the restoration process. However, the main disadvantage of this approach is its difficulty in applying it to large-scale systems due to the curse of dimensionality. This paper introduces the invalid action masking technique to overcome this limitation. The features of this technique include zero physical constraint violations, reduced training time, and stabilization of the explo- ration process. Simulation results are performed in IEEE 13-node and IEEE 123-node systems showing the better performance of the proposed algorithm in comparison to the conventional approaches both in terms of restored power and learning curve.

reinforcement learning, blackstart, artificial int↗
DOE OSTI2024

NuclPred v1

This tool takes a genome assembly as input and predicts per-site nucleosome occupancy as output. Trained on physical maps of nucleosome binding preferences across the fungal kingdom, NuclPred can be applied broadly across fungi (and other eukaryotes). This breadth, combined with its accuracy, means it could have both basic and applied biological implications, for example in understanding eukaryotic gene regulation and genetic engineering. Almost universally across eukaryotes, nucleosomes - each wrapping ~150 base pairs of DNA - serve to package DNA inside the nucleus, with major consequences on DNA access, gene activity and DNA integration. NuclPred was generated using a supervised deep learning approach combining convolutional and recurrent neural networks to take DNA features (nucleotides, GC content and structural information) as input, then use that information to predict the physical attractiveness DNA sequences might have for forming nucleosomes. With this information at hand, researchers can design more efficient CRISPR constructs, explore the interplay between DNA signatures and other regulators impact nucleosome locations, predict expression patterns, etc. This tool will be published as part of a manuscript currently under revision at iScience (draft attached).

Mondo, Stephen↗
NASA NTRS2008

A Multi-Scale Modeling System with Unified Physics

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 km, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. In recent years, exponentially increasing computer power has extended cloud-resolving-mode1 integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique.

Tao, Wei-Kuo↗
DOE OSTI2023

Fundamental aspects of Spacetime and Quantum Fields

The proposal contained two goals: firstly, placing fundamental bound on thermalization in Quantum Field Theories (QFTs) and, secondly, developing our understanding of emergent spacetime from matrices through concrete models. Since the previous reporting period, in collaboration with Sean Hartnoll we have continued our study of entanglement edge modes in matrix quantum mechanics (MQM). This has resulted in two papers. The first applies our construction for the Matrix Quantum Hall system first to fuzzy sphere states known to correspond to stringy M2-branes in MQM. Entanglement in these states using machine learning methods have also been studied by Sean Hartnoll and Xizhi Han in previous work done under this grant. Our construction builds on this work, and further demonstrates how area laws on fuzzy geometries emerge from strongly coupled systems. The second paper generalizes this construction to all noncommutative geometries with curvature much larger than the noncommutativity parameter. We demonstrate that despite UV/IR mixing effects, the structure of entanglement edge mode irreducible representations is determined by the boundary area of subsystems. On manifolds without global symmetries, we have demonstrated that nonlocal effects inherent to noncommutative geometries resum into a change of frame of the metric structure, similar to the change from string frame to Einstein frame for entanglement entropies calculated in string theory. These advancements lay the groundwork for future progress in the understanding of emergent geometry from large-N theories. Using these techniques, we are currently working on applying our methods to noncom mutative geometries whose construction is not so well understood, such as the fuzzy 5-sphere. Despite their opacity these objects are quite important, as string physics in the bulk of holographic systems bears many features of noncommutative geometry. We have also laid the groundwork of applying our methods to tensor networks, one of the most powerful models for understanding how geometry emerges from entanglement.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

A multilayer network analysis of Alzheimer's disease pathogenesis: Roles for p‐tau, synaptic peptides, and physical activity

INTRODUCTION: In the aging brain, cognitive abilities emerge from the coordination of complex pathways arising from a balance between protective lifestyle and environmental factors and accumulation of neuropathologies. METHODS: As part of the Rush Memory and Aging Project (n = 440), we measured accelerometer-based actigraphy, cognitive performance, and after brain autopsy, selected reaction monitoring mass spectrometry. Multilevel network analysis was used to examine the relationships among the molecular machinery of vesicular neurotransmission, Alzheimer's disease (AD) neuropathology, cognition, and late-life physical activity. RESULTS: Synaptic peptides involved in neuronal secretory function were the most influential contributors to the multilayer network, reflecting the complex interdependencies among AD pathology, synaptic processes, and late-life cognition. Older adults with lower physical activity evidenced stronger adverse relationships among phosphorylated tau peptides, markers of synaptic integrity, and tangle pathology. DISCUSSION: Network-based approaches simultaneously model interdependent biological processes and advance understanding of the role of physical activity in age-associated cognitive impairment. Highlights: Network-based approaches simultaneously model interdependent biological processes. Secretory synaptic peptides were influential contributors to the multilayer network. Older adults with lower physical activity had adverse relationships among pathology. There was interdependence among phosphorylated tau, synaptic integrity, and tangles. Network methods elucidate the role of physical activity in cognitive impairment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Find the Gap: Black Hole Population Analysis with an Astrophysically Motivated Mass Function

We introduce a novel black hole mass function that realistically models the physics of pair-instability supernovae with a minimal number of parameters. Applying this to all events in the LIGO-Virgo Gravitational-Wave Transient Catalog 2 (GWTC-2), we detect a peak at M BHMG = 46$^{+17}_{-6}$ M ⊙ . Repeating the analysis without the black holes from the event GW190521, we find this feature at M BHMG =54± 6 M ⊙ . These results establish the edge of the anticipated "black hole mass gap" at a value compatible with the expectation from standard stellar structure theory. The mass gap manifests itself as a discontinuity in the mass function and is populated by a distinct, less-abundant population of higher-mass black holes. We find that the primary black hole population scales with power-law index -1.95 ± 0.51 (-1.97 ± 0.44) with (without) GW190521, consistent with models of star formation. Using Bayesian techniques, we establish that our mass function fits a new catalog of black hole masses approximately as well as pre-existing phenomenological mass functions. We also remark on the implications of these results for constraining or discovering new phenomena in nuclear and particle physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Insights into Methodologies and Operational Details of Resource Adequacy Assessment: A Case Study with Application to a Broader Flexibility Framework

Assessing and maintaining resource adequacy (RA) is a core pillar of power systems. However, recent changes in the physical makeup of these systems and the conditions under which these systems must operate have yielded a renewed interest in the methods, metrics, and assumptions that underpin RA assessments. In this paper, we systematically explore a wide range of RA modeling dimensions, including: the objective function and level of operational detail in the underlying model formulation; the quantity (look-ahead) and quality (accuracy) of data that is available for making operational decisions within those models; and the physical configuration of solar photovoltaics (PV) with battery storage hybrid resources. We apply a set of probabilistic RA tools and production cost modeling tools to a realistic test system based loosely on a future Electric Reliability Council of Texas power system dominated by solar PV resources. Under the assumptions of our system and models, we find that multi-stage probabilistic assessments may provide a more robust evaluation of RA by capturing a wider range of operational and system interactions, but this comes at a computational cost of 1-2 orders of magnitude longer run time depending on the specific configuration. In addition, the information on thermal generator availability impacts RA performance by an order of magnitude more than solar resource forecasts, which is driven by the comparatively larger magnitude of thermal outages than solar forecast errors within our test system. Lastly, the flexibility provided by hybrid and other resources can help reduce system load-shedding event frequencies and enable the system to be more robust to inaccurate forecast information, and alternative hybrid inverter sizes can impact RA levels by 1-2 orders of magnitude. Our results point to the importance of a broader flexibility framework to describe the interaction between (1) flexibility "supply" from both physical resource capabilities and operational constraints considered in the modeling, and (2) flexibility "demand" from forecast errors, thermal generator outages, and other sources of uncertainty, as well as their RA impacts. Results are likely sensitive to the system buildout explored; future work could consider additional system configurations and conditions.

ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMI↗
NASA NTRS1992

Analytical multiple scattering correction to the Mie theory: Application to the analysis of the lidar signal

The contribution of the multiple scattering to the lidar signal is dependent on the optical depth tau. Therefore, the radar analysis, based on the assumption that the multiple scattering can be neglected is limited to cases characterized by low values of the optical depth (tau less than or equal to 0.1) and hence it exclude scattering from most clouds. Moreover, all inversion methods relating lidar signal to number densities and particle size must be modified since the multiple scattering affects the direct analysis. The essential requests of a realistic model for lidar measurements which include the multiple scattering and which can be applied to practical situations follow. (1) Requested are not only a correction term or a rough approximation describing results of a certain experiment, but a general theory of multiple scattering tying together the relevant physical parameter we seek to measure. (2) An analytical generalization of the lidar equation which can be applied in the case of a realistic aerosol is requested. A pure analytical formulation is important in order to avoid the convergency and stability problems which, in the case of numerical approach, are due to the large number of events that have to be taken into account in the presence of large depth and/or a strong experimental noise.

Flesia, C.↗
DOE OSTI2020

DCTRGAN: improving the precision of generative models with reweighting

Significant advances in deep learning have led to more widely used and precise neural network-based generative models such as Generative Adversarial Networks (Gans). We introduce a post-hoc correction to deep generative models to further improve their fidelity, based on the Deep neural networks using the Classification for Tuning and Reweighting (Dctr) protocol. The correction takes the form of a reweighting function that can be applied to generated examples when making predictions from the simulation. We illustrate this approach using Gans trained on standard multimodal probability densities as well as calorimeter simulations from high energy physics. We show that the weighted Gan examples significantly improve the accuracy of the generated samples without a large loss in statistical power. This approach could be applied to any generative model and is a promising refinement method for high energy physics applications and beyond.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Floquet engineering multichannel Kondo physics

Floquet engineering has been used to engineer novel interactions in uncorrelated and magnetic insulating materials. Here, we apply this technique to simple heavy fermion models, showing that driving these systems with periodic light introduces emergent Kondo screening channels with distinct symmetries, where the allowed channels are controlled by the light polarization. Furthermore, these systems can be driven through multi-channel degeneracy points, dynamically realizing multi-channel Kondo impurities and lattices that can be used to explore quantum critical phenomena and potentially enhance superconductivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2016

ASTM Committee C28: International Standards for Properties and Performance of Advanced Ceramics-Three Decades of High-Quality, Technically-Rigorous Normalization

Physical and mechanical properties and performance of advanced ceramics and glasses are difficult to measure correctly without the proper techniques. For over three decades, ASTM Committee C28 on Advanced Ceramics, has developed high-quality, technically-rigorous, full-consensus standards (e.g., test methods, practices, guides, terminology) to measure properties and performance of monolithic and composite ceramics that may be applied to glasses in some cases. These standards contain testing particulars for many mechanical, physical, thermal, properties and performance of these materials. As a result these standards are used to generate accurate, reliable, repeatable and complete data. Within Committee C28, users, producers, researchers, designers, academicians, etc. have written, continually updated, and validated through round-robin test programs, 50 standards since the Committee's founding in 1986. This paper provides a detailed retrospective of the 30 years of ASTM Committee C28 including a graphical pictogram listing of C28 standards along with examples of the tangible benefits of standards for advanced ceramics to demonstrate their practical applications.

properties↗
DOE OSTI2024

All-optical image denoising using a diffractive visual processor

Abstract Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images – implemented at the speed of light propagation within a thin diffractive visual processor that axially spans <250 × λ, where λ is the wavelength of light. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30–40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A clustering-based approach to ocean model–data comparison around Antarctica

The Antarctic Continental Shelf seas (ACSS) are a critical, rapidly changing element of the Earth system. Analyses of global-scale general circulation model (GCM) simulations, including those available through the Coupled Model Intercomparison Project, Phase 6 (CMIP6), can help reveal the origins of observed changes and predict the future evolution of the ACSS. However, an evaluation of ACSS hydrography in GCMs is vital: previous CMIP ensembles exhibit substantial mean-state biases (reflecting, for example, misplaced water masses) with a wide inter-model spread. Because the ACSS are also a sparely sampled region, grid-point-based model assessments are of limited value. Our goal is to demonstrate the utility of clustering tools for identifying hydrographic regimes that are common to different source fields (model or data), while allowing for biases in other metrics (e.g., water mass core properties) and shifts in region boundaries. We apply K-means clustering to hydrographic metrics based on the stratification from one GCM (Community Earth System Model version 2; CESM2) and one observation-based product (World Ocean Atlas 2018; WOA), focusing on the Amundsen, Bellingshausen and Ross seas. When applied to WOA temperature and salinity profiles, clustering identifies “primary” and “mixed” regimes that have physically interpretable bases. For example, meltwater-freshened coastal currents in the Amundsen Sea and a region of high-salinity shelf water formation in the southwestern Ross Sea emerge naturally from the algorithm. Both regions also exhibit clearly differentiated inner- and outer-shelf regimes. The same analysis applied to CESM2 demonstrates that, although mean-state model biases in water mass T–S characteristics can be substantial, using a clustering approach highlights that the relative differences between regimes and the locations where each regime dominates are well represented in the model. CESM2 is generally fresher and warmer than WOA and has a limited fresh-water-enriched coastal regimes. Given the sparsity of observations of the ACSS, this technique is a promising tool for the evaluation of a larger model ensemble (e.g., CMIP6) on a circum-Antarctic basis.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

Observations on the Application of Machine Learning Techniques to Aviation Operations

There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large database. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This talk describes issues to be addressed in applying either model-driven or data-driven methods. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The issues are illustrated by a detailed example and summary of current research in the area. The application of MLT to aviation operations falls into two categories: (a) based on the lack of a physics-based model, MLT is the favored approach and (b) marginal difference between regression methods using physics-based models and MLT. Further research is needed in the selection of MLT to critical aviation operations. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Sridhar, Banavar↗
NASA NTRS2020

Application of Machine Learning Techniques to Aviation Operations: A Case Study

There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large database. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This talk describes issues to be addressed in applying either model-driven or data-driven methods. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The issues are illustrated by a detailed example and summary of current research in the area. The application of MLT to aviation operations falls into two categories 58; (a) based on the lack of a physics-based model, MLT is the favored approach and (b) marginal difference between regression methods using physics-based models and MLT. Further research is needed in the selection of MLT to critical aviation operations. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Sridhar, Banavar↗
NASA NTRS2020

Application of Machine Learning Techniques to Aviation Operations: NASA Case Studies

There is an increasing interest in applying methods based on Machine Learning Techniques(MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large database. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This talk describes issues to be addressed in applying either model-driven or data-driven methods. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The issues relating to data, feature selection and validation of the models are illustrated by examining case studies of the application of MLT to problems in air traffic management at NASA. Further research is needed in the application of MLT to critical aviation operations. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Sridhar, Banavar↗
NASA NTRS1999

Chain Dynamics in Magnetorheological Suspensions

Magnetorheological (MR) suspensions are composed of colloidal particles which acquire dipole moments when subjected to an external magnetic field. At sufficient field strengths and concentrations, the dipolar particles rapidly aggregate to form long chains. Subsequent lateral cross-linking of the dipolar chains is responsible for a rapid liquid-to-solid-like rheological transition. The unique, magnetically-activated rheological properties of MR suspensions make them ideal for interfacing mechanical systems to electronic controls. Additionally, the ability to experimentally probe colloidal suspensions interacting through tunable anisotropic potentials is of fundamental interest. Our current experimental work has focused on understanding the fluctuations of dipolar chains. It has been proposed by Halsey and Toor (HT) that the strong Landau-Peierls thermal fluctuations of dipolar chains could be responsible for long-range attractions between chains. Such interactions will govern the long-time relaxation of MR suspensions. We have synthesized monodisperse neutrally buoyant MR suspensions by density matching stabilized ferrofluid emulsion droplets with D2O. This allows us to probe the dynamics of the dipolar chains using light scattering without gravitational, interfacial, and polydispersity effects to resolve the short-wavelength dynamics of the dipolar chains. We used diffusing wave spectroscopy to measure these dynamics. The particle displacements at short times that show an independence to the field strength, but at long times exhibit a constrained, sub-diffusive motion that slows as the dipole strength is increased. The experiments are in good qualitative agreement with Brownian dynamics simulations of dipolar chains. Although there have been several important and detailed studies of the structure and interactions in MR suspensions, there has not been conclusive evidence that supports or contradicts the HT model prediction that long-range interactions exist between fluctuating chains of dipolar particles. Resolving this issue would contribute greatly to the understanding of these interesting and important materials. We have begun to test the predictions of the HT model by both examining the dynamics of individual chains and by measuring the forces between dipolar chains directly to accurately and quantitatively assess the interactions that they experience. To do so, we employ optical trapping techniques and video-microscopy to manipulate and observe our samples on the microscopic level. With these techniques, it is possible to observe chains that are fluctuating freely in three-dimensions, independent of interfacial effects. More importantly, we are able to controllably observe the interactions of two chains at various separations to measure the force-distance profile. The techniques also allow us to study the mechanical properties of individual chains and chain clusters. Our work to this point has focused on reversibly-formed dipolar chains due to field induced dipoles where the combination of this chaining, the dipolar forces, and the hydrodynamic interactions that dictate the rheology of the suspensions. One can envision, however, many situations where optical, electronic, or rheological behavior may be optimized with magneto-responsive anisotropic particles. Chains of polarizable particles may have the best properties as they can coil and flex in the absence of a field and stiffen and orient when a field is applied. We have recently demonstrated a synthesis of stable, permanent paramagnetic chains by both covalently and physically linking paramagnetic colloidal particles. The method employed allows us to create monodisperse chains of controlled length. We observed the stability, field-alignment, and rigidity of this new class of materials. The chains may exhibit unique rheological properties in an applied magnetic field over isotropic suspensions of paramagnetic particles. They are also useful rheological models as bead-spring systems. These chains form the basis for our current experiments with optical traps.

Gast, A. P.↗