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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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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Anti-symmetric barron functions and their approximation with sums of determinants

A fundamental problem in quantum physics is to encode functions that are completely anti-symmetric under permutations of identical particles. The architecture of neural network models for the electron wave function typically comprises an equivariant component followed by a summation of determinants. The recently introduced Generic Antisymmetric (GA) block is designed to enhance the expressivity of such neural wave functions, and it was found that the 2-layer GA block achieved more accurate energies than the corresponding single-determinant FermiNet architecure, suggesting its promise as a way to improve the expressivity of neural wave functions. In this paper we show how the function expressed by the 2-layer GA block can be decomposed into a sum of determinants. We formalize this result by defining the antisymmetric Barron space as a generalized version of the 2-layer GA block and providing an appromation theorem for this function class. This result can be viewed as a negative result showing that the 2-layer GA block is not more expressive than using multiple determinants.

Abrahamsen, Nilin↗
DOE OSTI2024

Reduced-order model to approximate response matrices for filter stack spectrometers

We present a reduced-order model to calculate response matrices rapidly for filter stack spectrometers (FSSs). The reduced-order model allows response matrices to be built modularly from a set of pre-computed photon and electron transport and scattering calculations through various filter and detector materials. While these modular response matrices are not appropriate for high-fidelity analysis of experimental data, they encode sufficient physics to be used as a forward model in design optimization studies of FSSs, particularly for machine learning approaches that require sampling and testing a large number of FSS designs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Learning broken symmetries with approximate invariance

Recognizing symmetries in data allows for significant boosts in neural network training, which is especially important where training data are limited. In many cases, however, the exact underlying symmetry is present only in an idealized dataset, and is broken in actual data, due to asymmetries in the detector, or varying response resolution as a function of particle momentum. Standard approaches, such as data augmentation or equivariant networks fail to represent the nature of the full, broken symmetry, effectively overconstraining the response of the neural network. We propose a learning model which balances the generality and asymptotic performance of unconstrained networks with the rapid learning of constrained networks. This is achieved through a dual-subnet structure, where one network is constrained by the symmetry and the other is not, along with a learned symmetry factor. In a simplified toy example that demonstrates violation of Lorentz invariance, our model learns as rapidly as symmetry constrained networks but escapes its performance limitations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Multiscale Neural Networks for Approximating Green’s Functions

Neural networks (NNs) have been widely used to solve partial differential equations (PDEs) in the applications of physics, biology, and engineering. One effective approach for solving PDEs with a fixed differential operator is learning Green’s functions. However, Green’s functions are notoriously difficult to learn due to their poor regularity, which typically requires larger NNs and longer training times. In this work, we address these challenges by leveraging multiscale NNs to learn Green’s functions. Through theoretical analysis using multiscale Barron space methods and experimental validation, we show that the multiscale approach significantly reduces the necessary NN size and accelerates training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Data for Approximately 15% of Miscanthus Yield is Lost at Current Commercial Cutting Heights in Iowa

Various works have quantitatively characterized the effects of environmental and management factors on Miscanthus x giganteus Greef et Deu ( mxg ) yield and, therefore, anticipated land requirement per unit production. However, little work has addressed the effects of cutting height, which may significantly contribute to the difference between the standing aboveground biomass at harvest (i.e., biological yield) and harvested yield. This study quantitatively characterized the effect of cutting height using a replicated nitrogen trial of a 5-year-old mxg stand in southeast Iowa and related this information to observations of cutting height in nearby commercial fields. Nitrogen fertilizer did not significantly change the relationship of the stem segment mass to length, and overall, a 1-cm stem segment contributes 0.5% of the total stem biomass within the bottom 44 cm of the stem. This results in an average harvest loss of 15% of the aboveground standing biomass when cutting at 30 cm, typically seen in commercial mxg fields in eastern Iowa. Cutting height should be considered when accurately predicting commercial mxg harvest yields and changes in soil organic carbon in a commercial mxg agroecosystem.

Biomass Analytics↗
DOE OSTI2025

Characterizing Artificial Viscosity Parameters with Approximate Symmetries

We are often faced with trying to capture the physics of compressible shocks, which are governed by the Euler equations. However, Euler shocks are formally discontinuous at the shock front (translating to a step-function behavior of rel evant flow variables). This poses a practical problem for codes with finite-sized grid elements. As a result, one must make a concession in simulating the be havior of shocks within a discrete framework. In particular, we must blur, or ‘regularize’ Euler shocks so that they may be captured on a finite grid.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Extended Gutzwiller Approximation for Nonlocal Electron-Electron and Electron-Boson Correlations (I): The Theory

Understanding electron-electron and electron-photon correlations is central to uncovering the fundamental mechanisms governing material properties, particularly in systems where strong interactions give rise to emergent phenomena such as superconductivity, magnetism, and polaritonic effects. These correlations play a pivotal role in cavity quantum materials, where hybridized light-matter states enable quantum control over electronic properties. However, capturing both local and nonlocal correlations in these systems presents a significant theoretical challenge. In this work, we extend the Gutzwiller wavefunction method to include nonlocal electron-photon and electron-electron interactions, providing a unified framework to study the intricate interplay between these effects. Our approach accurately captures the long-range correlations induced by photon exchange, enabling the exploration of exotic quantum phases and the effects of cavity coupling on electronic structure. By benchmarking the method across coupling regimes, we reveal the critical role of nonlocal correlations in stabilizing phases, such as superconducting and insulating states, that are inaccessible through local interactions alone. This generalized Gutzwiller framework offers a versatile tool for understanding and designing materials that harness the transformative potential of strong light-matter coupling.

36 MATERIALS SCIENCE↗