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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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Stringy dynamics from an amplitudes bootstrap
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Bootstrapped Dimensional Crossover of a Spin Density Wave
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Exact solution of the frustrated Potts model with next-nearest-neighbor interactions in one dimension via AI bootstrapping
The one-dimensional (1D) 𝐽 1 −𝐽 2 𝑞-state Potts model is solved exactly for arbitrary 𝑞 by analytically block-diagonalizing the original 𝑞 2 ×𝑞 2 transfer matrix into a simple 2 × 2 maximally symmetric subspace, based on using OpenAI's reasoning model o3-mini-high to exactly solve the 𝑞 = 3 case. Furthermore, by matching relevant subspaces, we map the Potts model onto a simpler effective 1D 𝑞-state Potts model, where 𝐽 2 acts as the nearest-neighbor interaction and 𝐽 1 as an effective magnetic field, nontrivially generalizing a 56-year-old theorem previously limited to the simplest case (𝑞 = 2, the Ising model). Our exact results provide insights to phenomena such as atomic or electronic order stacking in layered materials and the emergence of dome-shaped phases in complex phase diagrams. In conclusion, this work is anticipated to fuel both research in 1D frustrated magnets for recently discovered finite-temperature application potentials and the fast moving topic area of AI in science.
A 2.1 GS/s 2-Channel Pipeline-SAR ADC With Speed-Enhanced Bootstrap Switch and Low-Latency SAR Logic
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Double Bootstrapping
This code performs a simulation experiment that involves (i) drawing random numbers from the normal distribution, (ii) resampling elements from arrays with replacement, and (iii) computing various quantities like mean, standard deviation, etc. Further information is available in section 4 of FERMILAB-FN-1273-ETD [https://inspirehep.net/literature/2925453].
Bell-State Bootstrapping via Spot-Checking
We present a spot-checking method to monitor and mitigate errors in imperfect entangled photon sources from semi-trusted third parties. We demonstrate that our method effectively mitigates polarization drifts and yields high-fidelity polarization-entangled Bell-state generation.
Beurling-Selberg extremization and modular bootstrap at high energies
We consider previously derived upper and lower bounds on the number of operators in a window of scaling dimensions [\Delta - \delta,\Delta + \delta] [ Δ − δ , Δ + δ ] at asymptotically large \Delta Δ in 2d unitary modular invariant CFTs. These bounds depend on a choice of functions that majorize and minorize the characteristic function of the interval [\Delta - \delta,\Delta + \delta] [ Δ − δ , Δ + δ ] and have Fourier transforms of finite support. The optimization of the bounds over this choice turns out to be exactly the Beurling-Selberg extremization problem, widely known in analytic number theory. We review solutions of this problem and present the corresponding bounds on the number of operators for any \delta \geq 0 δ ≥ 0 . When 2\delta \in \mathbb Z_{\geq 0} 2 δ ∈ ℤ ≥ 0 the bounds are saturated by known partition functions with integer-spaced spectra. Similar results apply to operators of fixed spin and Virasoro primaries in c>1 c > 1 theories.
Generalized Bootstrap AMG and AIR-AMG for coupled PDE systems with a focus on spacetime discretizatoins
The Pennsylvania State University (“Subcontractor”) has worked on the design of new algebraic, parallel, multilevel methods that obtain the full space and time solution of systems of PDEs. In particular, the PI and his collaborators explored semi-intrusive approaches based on algebraic multigrid (AMG). The focus of the research has been on the development of these techniques for the Euler equations in 1d and 2d. The overall research focused on the development of adaptive AIR (approximate ideal restriction) AMG solvers for these problems. The PI also explored the use of smoothed aggregation and root-node energy-based AMG solvers for these problems.
Allan Variance is Bootstrap Aggregation for Spectral Estimation
Characterization of clocks and inertial sensors, such as accelerometers and gyroscopes, typically includes Allan variance analysis. Allan variance is ubiquitous in timing and navigation communities which may appear niche compared with generalized spectral analysis. This note provides some motivation for Allan Variance for audiences more familiar with spectral analysis.
The scale enhanced wild bootstrap method for evaluating climate models using wavelets
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Bootstraps, finite energy sum rules and closed forms for the scattering amplitudes
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Radarclinometry - Bootstrapping the radar reflectance function from the image pixel-signal frequency distribution and an altimetry profile
A method for determining the dependence of radar backscatter on incidence angle that is applicable to the region corresponding to a particular radar image is derived. The method is based on enforcing mathematical consistency between the frequency distribution of the images' pixel signals and a one-dimensional frequency distribution of slope component, which is obtained from a radar or laser altimetry profile in or near the imaged area. To test the resulting algorithm, an arbitrarily selected reflectance function is used to generate an artificial radar image from a digitized topographic map of the Lake Champlain West quadrangle in the Adirondack Mountains, U.S. It is found that, for 99 percent of the data, the maximum error is 1 degree.
Bootstrap data methodology for sequential hybrid model building
A method for modeling engine operation comprising the steps of: 1. collecting a first plurality of sensory data, 2. partitioning a flight envelope into a plurality of sub-regions, 3. assigning the first plurality of sensory data into the plurality of sub-regions, 4. generating an empirical model of at least one of the plurality of sub-regions, 5. generating a statistical summary model for at least one of the plurality of sub-regions, 6. collecting an additional plurality of sensory data, 7. partitioning the second plurality of sensory data into the plurality of sub-regions, 8. generating a plurality of pseudo-data using the empirical model, and 9. concatenating the plurality of pseudo-data and the additional plurality of sensory data to generate an updated empirical model and an updated statistical summary model for at least one of the plurality of sub-regions.
Permutation Testing and Bootstrapping: NonParametric Approaches to Statistical Testing and Estimation
No abstract available