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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Stochastic Gause Predator-Prey model: Noise-induced extinctions and invariance

We consider the Gause predator-prey with general bounded or sub‑linear functional responses, – which includes those of Holling types Ⅰ–Ⅳ. – and multiplicative Gaussian noise. In contrast to previous studies, the prey in our model follows logistic dynamics while the predator's population is solely regulated by consumption of the prey. To ensure well-posedeness, we derive explicit Lyapunov‐type criteria ensuring global positivity and moment boundedness of solutions. We find conditions for noise‑induced extinctions, proving that stochasticity can drive either population to collapse even when the deterministic analogue predicts stable coexistence. In the case when the predator becomes extinct, we establish a limiting distribution for the predator's population. Last, for functional responses of Holling type Ⅰ, we provide sufficient conditions on the intensity of the noise for the existence and uniqueness of a stationary distribution.

Gause model↗
NASA NTRS1999

Maxwell Duality, Lorentz Invariance, and Topological Phase

We discuss the Maxwell electromagnetic duality relations between the Aharonov-Bohm, Aharonov-Casher, and He-McKellar-Wilkens topological phases, which allows a unified description of all three phenomena.

Maxwell Duality Lorentz Invariance Topological Pha↗
NASA NTRS2013

Lorentz Invariance Violation: the Latest Fermi Results and the GRB-AGN Complementarity

Because they are bright and distant, Gamma-ray Bursts (GRBs) have been used for more than a decade to test propagation of photons and to constrain relevant Quantum Gravity (QG) models in which the velocity of photons in vacuum can depend on their energy. With its unprecedented sensitivity and energy coverage, the Fermi satellite has provided the most constraining results on the QG energy scale so far. In this talk, the latest results obtained from the analysis of four bright GRBs observed by the Large Area Telescope will be reviewed. These robust results, cross-checked using three different analysis techniques set the limit on QG energy scale at E(sub QG,1) greater than 7.6 times the Planck energy for linear dispersion and E(sub QG,2) greater than 1.3 x 10(exp 11) gigaelectron volts for quadratic dispersion (95% CL). After describing the data and the analysis techniques in use, results will be discussed and confronted to latest constraints obtained with Active Galactic Nuclei.

Quantum gravity↗
DOE OSTI2024

Chemical and Electrochemical Characterization of Hot–Pressed Li 6 PS 5 Cl Solid State Electrolyte: Operating Pressure–Invariant High Ionic Conductivity

Sulfide solid state electrolytes (SSE) are among the most promising materials in the effort to replace liquid electrolytes, largely due to their comparable ionic conductivities. Among the sulfide SSEs, Argyrodites (Li 6 PS 5 X, X=Cl, Br, I) further stand out due to their high theoretical ionic conductivity (~1×10 –2 S cm –1 ) and interfacial stability against reactive metal anodes such as lithium. Generally, solid state electrolyte pellets are pressed from powder feedstock at room temperature, however, pellets fabricated by cold pressing consistently result in low bulk density and high porosity, facilitating interfacial degradation reactions and allowing dendrites to propagate through the pores and grain boundaries. Here, we demonstrate the mechanical and electrochemical implications of hot-pressing standalone LPSCl SSE pellets with near-theoretical ionic conductivity, superior cycling performance, and enhanced mechanical stability. X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and x-ray diffraction spectroscopy (XRD) analysis reveal no chemical changes to the Argyrodite surface after hot pressing up to 250°C. Furthermore, we use electrochemical impedance spectroscopy (EIS) to understand mechanical stability of Argyrodite SSE pellets as a function of externally applied pressure, demonstrating for the first time pressed standalone Argyrodite pellets with near-theoretical conductivities at external pressures below 14 MPa.

25 ENERGY STORAGE↗
DOE OSTI2024

Test of CP-invariance of the Higgs boson in vector-boson fusion production and in its decay into four leptons

A search for CP violation in the decay kinematics and vector-boson fusion production of the Higgs boson is performed in the H → ZZ * → 4 ℓ ( ℓ = e, μ ) decay channel. The results are based on proton-proton collision data produced at the LHC at a centre-of-mass energy of 13 TeV and recorded by the ATLAS detector from 2015 to 2018, corresponding to an integrated luminosity of 139 fb –1 . Matrix element-based optimal observables are used to constrain CP-odd couplings beyond the Standard Model in the framework of Standard Model effective field theory expressed in the Warsaw and Higgs bases. Differential fiducial cross-section measurements of the optimal observables are also performed, and a new fiducial cross-section measurement for vector-boson-fusion production is provided. All measurements are in agreement with the Standard Model prediction of a CP-even Higgs boson.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Heterogeneous Mixtures of Dictionary Functions to Approximate Subspace Invariance in Koopman Operators: Why Deep Koopman Operators Work

Abstract Koopman operators model nonlinear dynamics as a linear dynamic system acting on a nonlinear function as the state. This nonstandard state is often called a Koopman observable and is usually approximated numerically by a superposition of functions drawn from a dictionary . In a widely used algorithm, extended dynamic mode decomposition (EDMD), the dictionary functions are drawn from a fixed class of functions. Deep learning combined with EDMD has been used to learn novel dictionary functions in an algorithm called deep dynamic mode decomposition (deepDMD). The learned representation both (1) accurately models and (2) scales well with the dimension of the original nonlinear system. In this paper, we analyze the learned dictionaries from deepDMD and explore the theoretical basis for their strong performance. We explore State-Inclusive Logistic Lifting (SILL) dictionary functions to approximate Koopman observables. Error analysis of these dictionary functions show they satisfy a property of subspace approximation, which we define as uniform finite approximate closure. Typically, a Koopman dictionary’s nonlinear functions are homogeneous. In this paper, we discover that structured mixing of heterogeneous dictionary functions drawn from different classes of nonlinear functions achieve the same accuracy and dimensional scaling as the deep-learning-based deepDMD algorithm Yeung et al. ( In: 2019 American Control Conference (ACC), 2019). We specifically show this by building a heterogeneous dictionary comprised of SILL functions and conjunctive radial basis functions (RBFs). This mixed dictionary achieves similar accuracy and dimensional scaling to deepDMD with an order of magnitude reduction in parameters, while maintaining geometric interpretability. These results strengthen the viability of dictionary-based Koopman models to solving high-dimensional nonlinear learning problems.

Johnson, Charles A.↗
DOE OSTI2026

Optimal invariant sets for atomistic machine learning

The representation of atomic configurations for machine learning models has led to numerous sets of descriptors. However, many descriptor sets are incomplete and/or functionally dependent. Incomplete sets cannot faithfully represent atomic environments. Yet complete constructions often suffer from a high degree of functional dependence, where some descriptors are functions of others. These redundant descriptors do not improve discrimination between atomic environments. We employ pattern recognition techniques to remove dependent descriptors to produce the smallest possible set that satisfies completeness. We apply this in two ways: First, we refine an existing description, the atomic cluster expansion. Second, we augment an incomplete construction, yielding a new message-passing neural network architecture that can recognize up to 5-body patterns. This architecture shows strong accuracy on state-of-the-art benchmarks while retaining low computational cost. Our results demonstrate the utility of this strategy to optimize descriptor sets across a range of descriptors and application datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Helicity is a topological invariant of massless particles: C = - 2 h

There is an elementary but indispensable relationship between the topology and geometry of massive particles. The geometric spin s is related to the topological dimension of the internal space V by dim⁡ V = 2⁢s + 1. This breaks down for massless particles, which are geometrically characterized by their helicity ℎ, but all have 1D internal spaces. We show that a subtler relation exists between the topology and geometry of massless particles. Wave functions of massless particles are sections of nontrivial line bundles over the light cone whose topology is completely characterized by their first Chern number C. We prove that in general C = -2⁢ℎ. In doing so, we also exhibit a method of generating all massless bundle representations via an Abelian group structure of massless particles.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗