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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Generalized Quantum Convolution for Multidimensional Data

The convolution operation plays a vital role in a wide range of critical algorithms across various domains, such as digital image processing, convolutional neural networks, and quantum machine learning. In existing implementations, particularly in quantum neural networks, convolution operations are usually approximated by the application of filters with data strides that are equal to the filter window sizes. One challenge with these implementations is preserving the spatial and temporal localities of the input features, specifically for data with higher dimensions. In addition, the deep circuits required to perform quantum convolution with a unity stride, especially for multidimensional data, increase the risk of violating decoherence constraints. In this work, we propose depth-optimized circuits for performing generalized multidimensional quantum convolution operations with unity stride targeting applications that process data with high dimensions, such as hyperspectral imagery and remote sensing. We experimentally evaluate and demonstrate the applicability of the proposed techniques by using real-world, high-resolution, multidimensional image data on a state-of-the-art quantum simulator from IBM Quantum.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

The Computational Complexity of Multidimensional Persistence

We present findings on the computational complexity of computing multidimensional persistent homology. We first show that the worst-case computational complexity of multidimensional persistence is exponential. We then present an algorithm for computing multidimensional persistence which extends the algorithm given by Zomorodian and Carlsson for computing one-dimensional persistence. The computational complexity of our algorithm is polynomial in the size of the persistence module and exponential in the persistence dimension.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Hierarchical Self‐Assembly of Multidimensional Functional Materials from Sequence‐Defined Peptoids

Abstract Hierarchical self‐assembly represents a powerful strategy for the fabrication of functional materials across various length scales. However, achieving precise formation of functional hierarchical assemblies remains a significant challenge and requires a profound understanding of molecular assembly interactions. In this study, we present a molecular‐level understanding of the hierarchical assembly of sequence‐defined peptoids into multidimensional functional materials, including twisted nanotube bundles serving as a highly efficient artificial light harvesting system. By employing synchrotron‐based powder X‐ray diffraction and analyzing single crystal structures of model compounds, we elucidated the molecular packing and mechanisms underlying the assembly of peptoids into multidimensional nanostructures. Our findings demonstrate that incorporating aromatic functional groups, such as tetraphenyl ethylene (TPE), at the termini of assembling peptoid sequences promotes the formation of twisted bundles of nanotubes and nanosheets, thus enabling the creation of a highly efficient artificial light harvesting system. This research exemplifies the potential of leveraging sequence‐defined synthetic polymers to translate microscopic molecular structures into macroscopic assemblies. It holds promise for the development of functional materials with precisely controlled hierarchical structures and designed functions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Bounded-Confidence Models of Multidimensional Opinions with Topic-Weighted Discordance

People’s opinions on a wide range of topics often evolve over time through their interactions with others. Models of opinion dynamics primarily focus on one-dimensional opinions, which represent opinions on one topic. However, opinions on various topics are rarely isolated; instead, they can be interdependent and correlated. In a bounded-confidence model (BCM) of opinion dynamics, agents are receptive to each other only if their opinions are sufficiently similar. Here, we extend classical agent-based BCMs—namely, the Hegselmann–Krause BCM, which has synchronous interactions, and the Deffuant–Weisbuch BCM, which has asynchronous interactions—to a multidimensional setting, in which the opinions are multidimensional vectors representing opinions of different topics and opinions on different topics are interdependent. To measure opinion differences between agents, we introduce topic-weighted discordance functions that account for opinion differences in all topics. We define regions of receptiveness for our models, and we use them to characterize the steady-state opinion clusters and provide an analytical approach to compute these regions. In addition, we numerically simulate our models on various networks with initial opinions drawn from a variety of distributions. When initial opinions are correlated across different topics, our topic-weighted BCMs yield significantly different results in both transient and steady states compared to baseline models, where the dynamics of each opinion topic are independent.

Mathematics and Computing↗
DOE OSTI2024

Hierarchical Self‐Assembly of Multidimensional Functional Materials from Sequence‐Defined Peptoids

Abstract Hierarchical self‐assembly represents a powerful strategy for the fabrication of functional materials across various length scales. However, achieving precise formation of functional hierarchical assemblies remains a significant challenge and requires a profound understanding of molecular assembly interactions. In this study, we present a molecular‐level understanding of the hierarchical assembly of sequence‐defined peptoids into multidimensional functional materials, including twisted nanotube bundles serving as a highly efficient artificial light harvesting system. By employing synchrotron‐based powder X‐ray diffraction and analyzing single crystal structures of model compounds, we elucidated the molecular packing and mechanisms underlying the assembly of peptoids into multidimensional nanostructures. Our findings demonstrate that incorporating aromatic functional groups, such as tetraphenyl ethylene (TPE), at the termini of assembling peptoid sequences promotes the formation of twisted bundles of nanotubes and nanosheets, thus enabling the creation of a highly efficient artificial light harvesting system. This research exemplifies the potential of leveraging sequence‐defined synthetic polymers to translate microscopic molecular structures into macroscopic assemblies. It holds promise for the development of functional materials with precisely controlled hierarchical structures and designed functions.

Shao, Li↗
DOE OSTI2016

The Multidimensional Character of Nucleosynthesis in Core-Collapse Supernovae

Observations of supernovae and their remnants reveal highly aspherical distributions of the newly-formed elements that are the dying stars’ contributions to the interstellar medium. Modern simulations of the supernova’s neutrino-powered central engine reveal that these inhomogeneities originate in the first seconds of the explosions. Yet, much of our understanding of supernova nucleosynthesis is based on spherically symmetric models of the explosions. Recent simulations, combining high-fidelity treatments of the neutrino field that drives the explosion, the multidimensional fluid flow that taps this energy source, and the thermonuclear kinetics responsible for the composition of the ejecta, are revealing the limitations of our spherically symmetric understanding. Here, we highlight these recent results to presage the changes in our understanding of supernova nucleosynthesis that will result from a full appreciation of the multidimensional character of core-collapse supernovae.

Hix, W Raphael↗
DOE OSTI2019

New Examples of Integrable Systems with Dissipation on the Tangent Bundles of Multidimensional Spheres

In many problems of multidimensional dynamics, systems appear whose state spaces are spheres of finite dimension. Clearly, phase spaces of such systems are tangent bundles of these spheres. In this paper, we examine nonconservative force fields in the dynamics of a multidimensional rigid body in which the system possesses a complete set of first integrals that can be expressed as finite combinations of elementary transcendental functions. We consider the case where the moment of nonconservative forces depends on the tensor of angular velocity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Continuous-Time Multidimensional Walks as an Integrable Model

We consider continuous-time random walks on multidimensional symplectic lattices. It is shown that the generating functions of random walks and the transition amplitudes of continuous-time quantum walks can be expressed through dynamical correlation functions of an exactly solvable model describing strongly correlated bosons on a chain, the so-called phase model. The number of random lattice paths with a fixed number of steps connecting the starting and ending points on the multidimensional lattice is expressed through solutions of the Bethe equations of the phase model. Its asymptotics is obtained in the limit of a large number of steps.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Multidimensional molecular high-harmonic spectroscopy: A road map for charge migration studies

This work discusses recent experimental and theoretical analyses of high-harmonic spectroscopy in small molecules, with the aim of characterizing charge migration. We discuss the formulation of molecular high-harmonic spectra followed by methods to extract molecular-target-specific information, both in experiments and ab initio simulations. We present measured and simulated high-harmonic spectra from carbon dioxide and carbonyl sulfide to illustrate the necessity for multidimensional analyses for high-harmonic spectroscopies that include both the spectral amplitude and phase. Leveraging these results, we examine how such multidimensional analyses pave the way for the study of charge migration with high-harmonic spectroscopy and illustrate the beneficial role a static molecular feature can play when probing dynamics. Finally, we briefly expand our scope with an outlook on the critical role of integrating theoretical and experimental approaches, beyond just high-harmonic spectroscopy, for the development of versatile harmonic spectroscopic probes of charge migration.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

The development and applications of multidimensional biomolecular spectroscopy illustrated by photosynthetic light harvesting

The parallel and synergistic developments of atomic resolution structural information, new spectroscopic methods, their underpinning formalism, and the application of sophisticated theoretical methods have led to a step function change in our understanding of photosynthetic light harvesting, the process by which photosynthetic organisms collect solar energy and supply it to their reaction centers to initiate the chemistry of photosynthesis. The new spectroscopic methods, in particular multidimensional spectroscopies, have enabled a transition from recording rates of processes to focusing on mechanism. We discuss two ultrafast spectroscopies – two-dimensional electronic spectroscopy and two-dimensional electronic-vibrational spectroscopy – and illustrate their development through the lens of photosynthetic light harvesting. Both spectroscopies provide enhanced spectral resolution and, in different ways, reveal pathways of energy flow and coherent oscillations which relate to the quantum mechanical mixing of, for example, electronic excitations (excitons) and nuclear motions. The new types of information present in these spectra provoked the application of sophisticated quantum dynamical theories to describe the temporal evolution of the spectra and provide new questions for experimental investigation. While multidimensional spectroscopies have applications in many other areas of science, we feel that the investigation of photosynthetic light harvesting has had the largest influence on the development of spectroscopic and theoretical methods for the study of quantum dynamics in biology, hence the focus of this review. We conclude with key questions for the next decade of this review.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A Roadmap for Edge Computing Enabled Automated Multidimensional Transmission Electron Microscopy

The advent of modern, high-speed electron detectors has made the collection of multidimensional hyperspectral transmission electron microscopy datasets, such as 4D-STEM, a routine. However, many microscopists find such experiments daunting since analysis, collection, long-term storage, and networking of such datasets remain challenging. Some common issues are their large and unwieldy size that often are several gigabytes, non-standardized data analysis routines, and a lack of clarity about the computing and network resources needed to utilize the electron microscope. The existing computing and networking bottlenecks introduce significant penalties in each step of these experiments, and thus, real-time analysis-driven automated experimentation for multidimensional TEM is challenging. One solution is to integrate microscopy with edge computing, where moderately powerful computational hardware performs the preliminary analysis before handing off the heavier computation to high-performance computing (HPC) systems. In this work, we trace the roots of computation in modern electron microscopy, demonstrate deep learning experiments running on an edge system, and discuss the networking requirements for tying together microscopes, edge computers, and HPC systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Tensor-Train Split-Operator Fourier Transform (TT-SOFT) Method: Multidimensional Nonadiabatic Quantum Dynamics

We introduce the “tensor-train split-operator Fourier transform” (TT-SOFT) method for simulations of multidimensional nonadiabatic quantum dynamics. TT-SOFT is essentially the grid-based SOFT method implemented in dynamically adaptive tensor-train representations. In the same spirit of all matrix product states, the tensor-train format enables the representation, propagation, and computation of observables of multidimensional wave functions in terms of the grid-based wavepacket tensor components, bypassing the need of actually computing the wave function in its full-rank tensor product grid space. We demonstrate the accuracy and efficiency of the TT-SOFT method as applied to propagation of 24-dimensional wave packets, describing the S 1 / S 2 interconversion dynamics of pyrazine after UV photoexcitation to the S 2 state. Furthermore, our results show that the TT-SOFT method is a powerful computational approach for simulations of quantum dynamics of polyatomic systems since it avoids the exponential scaling problem of full-rank grid-based representations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

MZA: A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry

Modern mass spectrometry-based workflows employing hybrid instrumentation and orthogonal separations collect multidimensional data, potentially allowing deeper understanding in omics studies through adoption of artificial intelligence methods. However, the large volume of these rich data challenges existing data storage and access technologies, therefore precluding informatics advancements. Here we present MZA™ (pronounced m-za), the mass-to-charge (m/z) generic data storage and access tool designed to facilitate software development and artificial intelligence research in multidimensional mass spectrometry measurements. Composed by a data conversion tool and a simple file structure based on the HDF5 format, MZA provides easy, cross-platform and efficient programmatic access to raw MS-data, enabling fast development of new tools in data science programming languages such as Python and R. The software executable and example Python and R scripts are freely available at https://github.com/PNNL-m-q/mza.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Multidimensional Distributional Neural Network Output Demonstrated in Super‐Resolution of Surface Wind Speed

Accurate quantification of uncertainty in neural network predictions remains a central challenge for scientific applications involving high-dimensional, correlated data. While existing methods capture either aleatoric or epistemic uncertainty, few offer closed-form, multidimensional distributions that preserve spatial correlation while remaining computationally tractable. In this work, we present a framework for training neural networks with a multidimensional Gaussian loss, generating a closed-form predictive distribution over outputs informed by non-identically distributed training data. Our approach captures aleatoric uncertainty by iteratively estimating the means and covariance matrices, and is demonstrated on a super-resolution example out-of-training-sample. We leverage a Fourier representation of the covariance matrix to stabilize network training and preserve spatial correlation. We introduce a novel regularization strategy—referred to as information sharing—that interpolates between image-specific and global covariance estimates, enabling convergence of the super-resolution downscaling network trained on image-specific distributional loss functions. This framework allows for efficient sampling, explicit correlation modeling, and extensions to more complex distribution families all without disrupting prediction performance. We demonstrate the method on a surface wind speed downscaling task and discuss its broader applicability to uncertainty-aware prediction in scientific models.

17 WIND ENERGY↗
DOE OSTI2021

Multidimensional low-Mach number time-implicit hydrodynamic simulations of convective helium shell burning in a massive star

A realistic parametrization of convection and convective boundary mixing in conventional stellar evolution codes is still the subject of ongoing research. Furthermore, to improve the current situation, multidimensional hydrodynamic simulations are used to study convection in stellar interiors. Such simulations are numerically challenging, especially for flows at low Mach numbers which are typical for convection during early evolutionary stages. We explore the benefits of using a low-Mach hydrodynamic flux solver and demonstrate its usability for simulations in the astrophysical context. Simulations of convection for a realistic stellar profile are analyzed regarding the properties of convective boundary mixing. The time-implicit Seven-League Hydro (SLH) code was used to perform multidimensional simulations of convective helium shell burning based on a 25 M ⊙ star model. The results obtained with the low-Mach AUSM + -up solver were compared to results when using its non low-Mach variant AUSM B + -up. We applied well-balancing of the gravitational source term to maintain the initial hydrostatic background stratification. The computational grids have resolutions ranging from 180 × 90 2 to 810 × 540 2 cells and the nuclear energy release was boosted by factors of 3 × 10 3 , 1 × 10 4 , and 3 × 10 4 to study the dependence of the results on these parameters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Do not forget the electrons: Extending moderately-sized nuclear networks for multidimensional hydrodynamic codes

Context.Nuclear networks are widely used coupled with hydrodynamical simulations of explosive scenarios to account for the change of nuclear species and energy generation rate due to nuclear reactions. In this way, there is a feedback mechanism between the hydrodynamical state and the nuclear processes. Unfortunately, the timescale of nuclear reactions is orders of magnitude smaller than the dynamical timescale that drives hydrodynamical simulations. Therefore, these nuclear networks are usually very small, reduced in most cases to a dozen elements, especially when simulations are carried out in more than one dimension. Aims.We present here an extended nuclear network, with 90 species, designed for being coupled with hydrodynamic simulations, which includes neutrons, protons, electrons, positrons, and the corresponding neutrino and anti-neutrino emission. This network is also coupled with temperature, making it extremely robust and, together with its size, unique of its kind. The inclusion of electron captures on free protons makes the network very appropriate for multidimensional studies of Type Ia supernova explosions, especially when the exploding object is a massive white dwarf. Methods.We perform several tests that are relevant to simulate explosive scenarios, such as Type Ia supernovae and core-collapse supernovae. We compare the results of the 90 nuclei network with a standardα-chain network with 14 elements to evaluate the differences in the energy generation rate. We also evaluate the relevance of including the electrons in the network in terms of generated yields and how it affects the pressure of a degenerate fluid such as that of white dwarfs. The results obtained with the 90-nuclei network have been verified with a much larger 2000-nuclei network built from REACLIB (WinNet), in terms of nuclear energy generation rate, pressure, and produced yields. Results.The results obtained with the proposed medium-sized network compare fairly well, to a few percent, with those computed withWinNetin scenarios reproducing the gross physical conditions of current Type Ia supernova explosion models. In those cases where the carbon and oxygen fuel ignites at high density, the high-temperature plateau typical of the nuclear statistical equilibrium regime is well defined and stable, allowing large integration time steps. We show that the inclusion of electron captures on free protons substantially improves the estimation of the electron fraction of the mixture. Therefore, the pressure is better determined than in networks where electron captures are excluded, which will ultimately lead to more reliable hydrodynamic models. Explosive combustion of helium at low density, occurring near the surface layer of a white dwarf, is also better described with the proposed network, which gives nuclear energy generation rates much closer toWinNetthan typical reduced alpha networks. Conclusions.A nuclear network withN= 90 species, including electrons, aimed at multidimensional calculations of supernova explosions is described and verified. The proposed network is suitable for the study of Type Ia supernova explosions because it provides better values of pressure and electron abundance than other existing networks with smaller or even a similar size but without including electron capture processes.

Astronomy & Astrophysics↗
DOE OSTI2020

Multimodal, Multidimensional, and Multiscale X-ray Imaging at the National Synchrotron Light Source II

Over the last couple of decades, the synchrotron radiation research community has witnessed tremendous advancement in the field of x-ray imaging and microscopy. Continuing enhancement of the light sources’ brightness, advances in x-ray focusing optics, incorporation of precision instruments, and development of innovative imaging techniques are some of the leading contributors for the rapid progress. New imaging and microscopy beamlines, such as the ones at the National Synchrotron Light Source II (NSLS-II), a Department of Energy (DOE) Office of Science user facility located at DOE’s Brookhaven National Laboratory, are capable of performing more sophisticated and complicated measurements than ever before, either on their own or used together as a suite of tools. These sophisticated and complex measurements exhibit attributes for multimodal, multidimensional, and multiscale imaging. Recent popularity of these methods is strongly driven by the current trends in materials characterizations, where researchers desire to map out hierarchical materials structure over a large range of length scales, to understand structure-property correlation, to quantify materials structures in 3D, and/or to perform operando or in situ experiments. It is important to emphasize that “materials” under investigation are not only synthesized materials but also the natural materials. In this article, we describe multimodal, multidimensional and multiscale x-ray imaging capabilities of the NSLS-II beamlines and how these methods are used to tackle complex scientific problems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

TTHRESH: Tensor Compression for Multidimensional Visual Data

Memory and network bandwidth are decisive bottlenecks when handling high-resolution multidimensional data sets in visualization applications, and they increasingly demand suitable data compression strategies. In this paper, we introduce a novel lossy compression algorithm for multidimensional data over regular grids. It leverages the higher-order singular value decomposition (HOSVD), a generalization of the SVD to three dimensions and higher, together with bit-plane, run-length and arithmetic coding to compress the HOSVD transform coefficients. Our scheme degrades the data particularly smoothly and achieves lower mean squared error than other state-of-the-art algorithms at low-to-medium bit rates, as it is required in data archiving and management for visualization purposes. Further advantages of the proposed algorithm include very fine bit rate selection granularity and the ability to manipulate data at very small cost in the compression domain, for example to reconstruct filtered and/or subsampled versions of all (or selected parts) of the data set.

97 MATHEMATICS AND COMPUTING↗