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At least 613 records · Page 34

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Effective many-body interactions in reduced-dimensionality spaces through neural network models

Accurately describing properties of challenging problems in physical sciences often requires complex mathematical models that are unmanageable to tackle head on. Therefore, developing reduced-dimensionality representations that encapsulate complex correlation effects in many-body systems is crucial to advance the understanding of these complicated problems. However, a numerical evaluation of these predictive models can still be associated with a significant computational overhead. To address this challenge, in this paper we discuss a combined framework that integrates recent advances in the development of active-space representations of coupled cluster (CC) downfolded Hamiltonians with neural network approaches. The primary objective of this effort is to train neural networks to eliminate the computationally expensive steps required for evaluating hundreds or thousands of Hugenholtz diagrams, which correspond to multidimensional tensor contractions necessary for evaluating a many-body form of downfolded effective Hamiltonians. Using small molecular systems (the H 2 O and HF molecules) as examples, we demonstrate that training neural networks employing effective Hamiltonians for a few nuclear geometries of molecules can accurately interpolate or extrapolate their forms to other geometrical configurations characterized by different intensities of correlation effects. We also discuss differences between effective interactions that define CC downfolded Hamiltonians with those of bare Hamiltonians defined by Coulomb interactions in the active spaces. Published by the American Physical Society 2024

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Maximum Entropy Principle in Deep Thermalization and in Hilbert-Space Ergodicity

We report universal statistical properties displayed by ensembles of pure states that naturally emerge in quantum many-body systems. Specifically, two classes of state ensembles are considered: those formed by (i) the temporal trajectory of a quantum state under unitary evolution or (ii) the quantum states of small subsystems obtained by partial, local projective measurements performed on their complements. These cases, respectively, exemplify the phenomena of “Hilbert-space ergodicity” and “deep thermalization.” In both cases, the resultant ensembles are defined by a simple principle: The distributions of pure states have maximum entropy, subject to constraints such as energy conservation, and effective constraints imposed by thermalization. We present and numerically verify quantifiable signatures of this principle by deriving explicit formulas for all statistical moments of the ensembles, proving the necessary and sufficient conditions for such universality under widely accepted assumptions, and describing their measurable consequences in experiments. We further discuss information-theoretic implications of the universality: Our ensembles have maximal information content while being maximally difficult to interrogate, establishing that generic quantum state ensembles that occur in nature hide (scramble) information as strongly as possible. Our results generalize the notions of Hilbert-space ergodicity to time-independent Hamiltonian dynamics and deep thermalization from infinite to finite effective temperature. Our work presents new perspectives to characterize and understand universal behaviors of quantum dynamics using statistical and information-theoretic tools.

Eigenstate thermalization↗

DOE OSTI2023

Phase-Space Geometry and Optimal State Preparation in Quantum Metrology with Collective Spins

We revisit well-known protocols in quantum metrology using collective spins and propose a unifying picture for optimal state preparation based on a semiclassical description in phase space. We show how this framework allows for quantitative predictions of the timescales required to prepare various metrologically useful states, and that these predictions remain accurate even for moderate system sizes, surprisingly far from the classical limit. Furthermore, this framework allows us to build a geometric picture that relates optimal (exponentially fast) entangled probe preparation to the existence of separatrices connecting saddle points in phase space. We illustrate our results with the paradigmatic examples of the two-axis countertwisting and twisting-and-turning Hamiltonians, where we provide analytical expressions for all the relevant optimal timescales. Finally, we propose a generalization of these models to include p-body collective interaction (or p-order twisting), beyond the usual case of p = 2. Using our geometric framework, we prove a no-go theorem for the local optimality of these models for p > 2.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Directional Superradiance in a Driven Ultracold Atomic Gas in Free Space

Ultracold atomic systems are among the most promising platforms that have the potential to shed light on the complex behavior of many-body quantum systems. One prominent example is the case of a dense ensemble illuminated by a strong coherent drive while interacting via dipole-dipole interactions. Despite being subjected to intense investigations, this system retains many open questions. A recent experiment carried out in a pencil-shaped geometry [Ferioli Nat. Phys. 19, 1345 (2023)] has reported measurements that have seemed consistent with the emergence of strong collective effects in the form of a “superradiant” phase transition in free space, when looking at the light-emission properties in the forward direction. Motivated by the experimental observations, we carry out a systematic theoretical analysis of the steady-state properties of the system as a function of the driving strength and atom number N . We observe signatures of collective effects in the weak-driving regime, which disappear with increasing drive strength as the system evolves into a single-particle-like mixed state comprised of randomly aligned dipoles. Although the steady state features some similarities to the reported superradiant-to-normal nonequilibrium transition, also known as cooperative resonance fluorescence, we observe significant qualitative and quantitative differences, including a different scaling of the critical drive parameter (from N to N ). We validate the applicability of a mean-field treatment to capture the steady-state dynamics under currently accessible conditions. Furthermore, we develop a simple theoretical model that explains the scaling properties by accounting for interaction-induced inhomogeneous effects and spontaneous emission, which are intrinsic features of interacting disordered arrays in free space. Published by the American Physical Society 2024

Agarwal, Sanaa (ORCID:0000000336863804)↗

DOE OSTI2025

Single-Shot Reconstruction of Electron Beam Longitudinal Phase Space in a Laser Wakefield Accelerator

We report on a single-shot longitudinal phase-space reconstruction diagnostic for electron beams in a laser wakefield accelerator via the experimental observation of distinct periodic modulations in the angularly resolved spectra. Such modulated angular spectra arise as a result of the direct interaction between the ultrarelativistic electron beam and the laser driver in the presence of the wakefield. A constrained theoretical model for the coupled oscillator, assisted by a genetic algorithm, can recreate the experimental electron spectra and, thus, fully reconstructs the longitudinal phase-space distribution of the electron beam with a temporal resolution of approximately 1.3 fs. In particular, it reveals the slice energy spread of the electron beam, which is important to measure for applications such as x-ray free electron lasers. In our experiment, the root-mean-square energy spread retrieved is bounded at 9.9 MeV, corresponding to a 0.9%–3.0% relative spread, despite the overall GeV energy beam having approximately 100% relative energy spread.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

K-space algorithmic reconstruction (KAREN): a robust statistical methodology to separate Bragg and diffuse scattering

Diffuse scattering occurring in the Bragg diffraction pattern of a long-range-ordered structure represents local deviation from the governing regular lattice. However, interpreting the real-space structure from the diffraction pattern presents a significant challenge because of the dramatic difference in intensity between the Bragg and diffuse components of the total scattering function. In contrast to the sharp Bragg diffraction, the diffuse signal has generally been considered to be a weak expansive or continuous background signal. In this paper, using 1D and 2D models, it is demonstrated that diffuse scattering in fact consists of a complex array of high-frequency features that must not be averaged into a low-frequency background signal. To evaluate the actual diffuse scattering effectively, an algorithm has been developed that uses robust statistics and traditional signal processing techniques to identify Bragg peaks as signal outliers which can be removed from the overall scattering data and then replaced by statistically valid fill values. This method, described as a 'K-space algorithmic reconstruction' (KAREN), can identify Bragg reflections independent of prior knowledge of a system's unit cell. KAREN does not alter any data other than that in the immediate vicinity of the Bragg reflections, and reconstructs the diffuse component surrounding the Bragg peaks without introducing discontinuities which induce Fourier ripples or artifacts from underfilling `punched' voids. The KAREN algorithm for reconstructing diffuse scattering provides demonstrably better resolution than can be obtained from previously described punch-and-fill methods. The superior structural resolution obtained using the KAREN method is demonstrated by evaluating the complex ordered diffuse scattering observed from the neutron diffraction of a single plastic crystal of CBr 4 using pair distribution function analysis.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Pressure cells for in situ neutron total scattering: time and real-space resolution during deuterium absorption

In situ gas-loading sample holders for two-dimensionally arranged detectors in time-of-flight neutron total scattering experiments have been developed to investigate atomic arrangements during deuterium absorption using time and real-space resolution. A single-crystal sapphire container was developed that allows conditions of 473 K and 10 MPa hydrogen gas pressure. High-resolution transient measurements detected deuterium absorption by palladium that proceeded within a few seconds. A double-layered container with thick- and thin-walled vanadium allowed conditions of 423 K and 10 MPa hydrogen gas pressure. The deuterium occupation sites of a lanthanum–nickel–aluminium alloy are discussed in detail on the basis of real-space high-resolution data obtained from in situ neutron scattering measurements and reverse Monte Carlo structural modeling.

08 HYDROGEN↗

DOE OSTI2025

Growth functions of periodic space tessellations

This work analyzes the rules governing the growth of the numbers of vertices, edges and faces in all possible periodic tessellations of the 2D Euclidean space, and encodes those rules in several types of polynomial growth functions. These encodings map the geometric, combinatorial and topological properties of the tessellations into sets of integer coefficients. Several general statements about these encodings are given with rigorous mathematical proof. The variation of the growth functions is represented graphically and analyzed in orphic diagrams, so named because of their similarity to orphic art. Several examples of 3D space groups are included, to emphasize the complexity of the growth functions in higher dimensions. A freely available Python library is presented to facilitate the discovery of the growth functions and the generation of orphic diagrams.

Chemistry↗

DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗

DOE OSTI2020

A Computational Efficient Space-Vector Modulation Scheme for A Hybrid Seven-Level Converter for Medium Voltage Grid-Tied Applications

In this paper, a fast space-vector modulation (SVM) scheme is proposed for a hybrid 7-level (7-L) converter, i.e., cascaded T-type converter and H-bridge converters, for grid-tied applications. Using oblique coordinate reference, the proposed SVM can quickly locate the switching triangle enclosing the reference vector in the 7-L space vector diagram. Then all available switching sequences and vector duty cycles are acquired by straightforward calculation without using any look-up tables, which significantly reduces the computational cost. The floating capacitor voltages are also stabilized by using the proposed SVM. Experimental studies on a scale-down prototype are presented in this work to validate the effectiveness of the proposed scheme.

Diao, Fei↗

DOE OSTI2023

TopFusion: Using Topological Feature Space for Fusion and Imputation in Multi-Modal Data

We present a novel multi-modal data fusion technique using topological features. The method, TopFusion, leverages the flexibility of topological data analysis tools (namely persistent homology and persistence images) to map multi-modal datasets into a common feature space by forming a new multi-channel persistence image. Each channel in the image is representative of a view of the data from a modality-dependent filtration. We demonstrate that the topological perspective we take allows for more effective data reconstruction, i.e. imputation. In particular, by performing imputation in topological feature space we are able to outperform the same imputation techniques applied to raw data or alternatively derived features. We show that TopFusion representations can be used as input to downstream deep learning-based computer vision models and doing so achieves comparable performance to other fusion methods for classification on two multi-modal datasets.

Myers, Audun D.↗

DOE OSTI2023

Power Electronic Substrates for Space Restricted Application

High-power modules use substrates to house the semiconductor device and for electrical insulation. These substrates are constructed with thermally conductive dielectric material sandwiched between two metals to extract heat from semiconductor chips. Thus, the required cooling performance of a power module is linked to the substrate’s thermal performance and can vary based on the substrate technologies. In this study, four substrate technologies were evaluated for space-restricted applications: direct-bonded copper, an insulated metal substrate, a thermally annealed pyrolytic graphite–based insulated metal substrate, and direct-bonded aluminum, where the heat sink is directly attached without thermal interface materials. The finite element results suggest that the popular direct-bonded copper substrate has better thermal performance than the rest of the substrates for space-restricted applications. Furthermore, a modified direct-bonded copper is proposed to further improve thermal performance. The evaluation results show that the modified substrate can handle 30% more losses than its traditional counterpart.

Chowdhury, Shajjad↗

DOE OSTI2024

Fast Iterative Multi-site Hosting Capacity Analysis for Distribution Systems With Search Space Pruning

Interconnection studies for distributed energy resources (DERs) is a time-intensive process, primarily due to the necessity of solving large number of power flow scenarios. Hosting capacity analysis (HCA) is a time-consuming aspect of interconnection studies that is divided into single-site HCA (SHCA) and multi-site HCA (MHCA). From a computational and understandable standpoint, the industry seeks iteration-based solutions for SHCA, although it doesn't maximize the total DER hosting capacity (DERHC) of the grid, as MHCA does. While non-iterative solutions are available for MHCA, they involve a trade-off between the modeling accuracy of the distribution system, solution quality, and ease of understanding. In this work, we present a fast iterative solution for MHCA, reducing computational complexity by eliminating the need to solve power flows for a large amount of search space, thus making iterative solutions feasible. This iterative approach guarantees both a global optimal solution with sufficient time and a fast, close-to-optimal solution through efficient search space pruning. It also easily integrates with existing utility HCA tools. The results are demonstrated on select locations in the IEEE-123 bus system for community-scale interconnection studies. We highlight the benefits of skipping the need to solve millions of power flows, all while maximizing the grid's total DERHC.

Guddanti, Kishan Prudhvi↗

DOE OSTI2025

Theoretical investigation of plasma wave generation by pulsed electron beams in space

Here, we report theoretical calculations of plasma wave generation in the whistler modes and in the extraordinary modes, by pulsed electron beams in a magnetized plasma. The numerical simulations of the wave generation take into account the longitudinal expansion of the electron beam due to the space charge force and the energy spread. The work presented in this article provides predictions for the wave generation performance of the beam plasma interactions experiment (Beam PIE), where pulsed electron beams were produced by a spaceborne radio frequency (RF) linear accelerator. We also theoretically explore the desirable properties of the pulsed electron beam for future space experiments, which will be the next step toward eventually demonstrating the radiation-belt remediation (RBR).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

A Modified DBC Substrate Improving Thermal Performance for Confined Space Applications

High-power modules use substrates to house the semiconductor device and for electrical insulation. These substrates are constructed with thermally conductive dielectric material sandwiched between two metals to extract heat from semiconductor chips. Thus, the required cooling performance of a power module is linked to the substrate’s thermal performance and can vary based on the substrate technologies. Here, in this study, five substrate technologies were evaluated for space-restricted applications: direct-bonded copper (DBC), an insulated metal substrate (IMS), a thermally annealed pyrolytic graphite (TPG)-based IMS, DBC-based double-sided cooling, and direct-bonded aluminum (DBA) where the heat sink is directly attached without thermal interface materials (TIMs). The finite element (FE) analysis results suggest that the popular DBC substrate has thermal performance better than that of the other substrates for space-constrained applications. To further improve the thermal performance, a modified DBC substrate was proposed where a copper block was added between the semiconductor and the DBC substrate to achieve heat spreading underneath the chip. The modified DBC performance was then compared with the aforementioned substrates and the results showed significant thermal performance improvement. The results were verified with experimental results where the proposed substrate showed 20% more loss handling capability compared to an identical DBC substrate.

42 ENGINEERING↗

DOE OSTI2020

Multichannel spectral estimation in acoustics: A state-space approach

Spectral estimation is a necessary methodology to analyze the frequency content of noisy data sets especially in acoustic applications. Many spectral techniques have evolved starting with the classical Fourier transform methods based on the well-known Wiener-Khintchine relationship relating the covariance-to-spectral density as a transform pair culminating with more elegant model-based parametric techniques that apply prior knowledge of the data to produce a high-resolution spectral estimate. Multichannel spectral representations are a class of both nonparametric, as well as parametric, estimators that provide improved spectral estimates. In any case, classical nonparametric multichannel techniques can provide reasonable estimates when coupled with peak-peaking methods as long as the signal levels are reasonably high. Furthermore, parametric multichannel methods can perform quite well in low signal level environments even when applying simple peak-picking techniques. In this paper, the performance of both nonparametric (periodogram) and parametric (state-space) multichannel spectral estimation methods are investigated when applied to both synthesized noisy structural vibration data as well as data obtained from a sounding rocket flight. It is demonstrated that for the multichannel problem, state-space techniques provide improved performance, offering a parametric alternative compared to classical methods.

42 ENGINEERING↗

DOE OSTI2020

The fungal collaboration gradient dominates the root economics space in plants

Plant economics run on carbon and nutrients instead of money. Leaf strategies aboveground span an economic spectrum from “live fast and die young” to “slow and steady,” but the economy defined by root strategies belowground remains unclear. Here, we take a holistic view of the belowground economy and show that root-mycorrhizal collaboration can short circuit a one-dimensional economic spectrum, providing an entire space of economic possibilities. Root trait data from 1810 species across the globe confirm a classical fast-slow “conservation” gradient but show that most variation is explained by an orthogonal “collaboration” gradient, ranging from “do-it-yourself” resource uptake to “outsourcing” of resource uptake to mycorrhizal fungi. This broadened “root economics space” provides a solid foundation for predictive understanding of belowground responses to changing environmental conditions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Conservative and Accurate Solution Transfer Between High-Order and Low-Order Refined Finite Element Spaces

Here, in this paper, we introduce general transfer operators between high-order and low-order refined finite element spaces that can be used to couple high-order and low-order simulations. Under natural restrictions on the low-order refined space we prove that both the high-to-low-order and low-to-high-order linear mappings are conservative, constant preserving, and high-order accurate. While the proof holds for affine geometries, numerical experiments indicate that the results hold for more general curved meshes. We present several numerical results confirming our analysis and demonstrate the utility of the new mappings in the context of adaptive mesh refinement and conservative multidiscretization coupling.

97 MATHEMATICS AND COMPUTING↗