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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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Continuous Symmetries and Approximate Quantum Error Correction
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Automated Discovery of Autonomous Quantum Error Correction Schemes
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A Taxonomy of Small Markovian Errors
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Tomographic Volumetric Additive Manufacturing of Silica Glass with In Situ Error Calculation and Projection Tiling
The optical, thermal, and chemical properties of glass make it an ideal material for use in a wide range of applications, from optics and chemistry reactionware to consumer products. Tomographic volumetric additive manufacturing (tVAM) creates 3D objects by selectively gelling material within a rotating vial of photo-sensitive material by exposure to a series of tomographically-determined images. Here, in this study, tVAM is investigated for fabrication of complex, centimeter-scale, 3D silica glass parts. By optimizing the post-processing procedure, fully-dense glass parts with up to 2 cm 2 cross sectional area are achieved. The changes in optical properties during printing are leveraged for in situ metrology during printing, and the non-Newtonian nature of the glass-filled photoresin system to facilitate larger fabrication volumes via tiled projections. Printed structures are finally demonstrated for applications as MEMS components, optics, fluidics, and consumer products.
CheKiPEUQ Intro 1: Bayesian Parameter Estimation Considering Uncertainty or Error from both Experiments and Theory**
A common goal is extraction of physico-chemical parameter values such as pre-exponentials and activation energies from experiment. Ever increasing knowledge from experiments and computations is enabling semi-quantitative prior predictions of such values. When prior knowledge of physically realistic ranges is available, a method named Bayesian parameter estimation (BPE) enables more physically realistic parameter estimation relative to unsophisticated fitting by seeking the most probable value when considering together the uncertainties from prior knowledge, experimental data, and approximations in the model. An impediment to widespread use of BPE is a lack of understanding, training, and user-friendly software. Along with this invited publication, a general software package for BPE is being released that is user-friendly and that does not require understanding of the math behind the methodology. Overall, two previously unpublished catalysis science examples are provided along with considerations and guidelines for successful application of BPE. Following this work, BPE can become more widespread to enable extraction of physically meaningful parameter values.
Using Modal Projection Error to Predict Success of a Six Degree of Freedom Shaker Test.
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Precision-aware deterministic and probabilistic error bounds for floating point summation
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On the Trotter Error in Many-body Quantum Dynamics with Coulomb Potentials
Efficient simulation of many-body quantum systems is central to advances in physics, chemistry, and quantum computing, with a key question being whether the simulation cost scales polynomially with the system size. Here, in this work, we analyze many-body quantum systems with Coulomb interactions, which are fundamental to electronic and molecular systems. We prove that Trotterization for such unbounded Hamiltonians achieves a 1/4-order convergence rate, with explicit polynomial dependence on the number of particles. The result holds for all initial wavefunctions in the domain of the Hamiltonian, and the 1/4-order convergence rate is optimal, as previous work has numerically demonstrated that it can be saturated by a specific initial ground state. The main challenges arise from the many-body structure and the singular nature of the Coulomb potential. Our proof strategy differs from prior state-of-the-art Trotter analyses, addressing both difficulties in a unified framework. Our analysis treats the Coulomb potential as an unbounded operator without modification or regularization, and does not rely on spatial discretization, making it compatible with both first- and second-quantized circuit constructions.
Correcting energy estimation errors due to finite sampling of transition edge sensor data
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Built environment, driving errors and violations, and crashes in naturalistic driving environment
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Measurement of focal plane error in laser powder bed fusion machines
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Computationally efficient and error aware surrogate construction for numerical solutions of subsurface flow through porous media
Limiting the injection rate to restrict the pressure below a threshold at a critical location can be an important goal of simulations that model the subsurface pressure between injection and extraction wells. The pressure is approximated by the solution of Darcy’s partial differential equation for a given permeability field. The subsurface permeability is modeled as a random field since it is known only up to statistical properties. This induces uncertainty in the computed pressure. Solving the partial differential equation for an ensemble of random permeability simulations enables estimating a probability distribution for the pressure at the critical location. These simulations are computationally expensive, and practitioners often need rapid online guidance for real-time pressure management. An ensemble of numerical partial differential equation solutions is used to construct a Gaussian process regression model that can quickly predict the pressure at the critical location as a function of the extraction rate and permeability realization. The Gaussian process surrogate analyzes the ensemble of numerical pressure solutions at the critical location as noisy observations of the true pressure solution, enabling robust inference using the conditional Gaussian process distribution. Our first novel contribution is to identify a sampling methodology for the random environment and matching kernel technology for which fitting the Gaussian process regression model scales as O ( n log n ) instead of the typical O ( n 3 ) rate in the number of samples n used to fit the surrogate. The surrogate model allows almost instantaneous predictions for the pressure at the critical location as a function of the extraction rate and permeability realization. Our second contribution is a novel algorithm to calibrate the uncertainty in the surrogate model to the discrepancy between the true pressure solution of Darcy’s equation and the numerical solution. Finally, although our method is derived for building a surrogate for the solution of Darcy’s equation with a random permeability field, the framework broadly applies to solutions of other partial differential equations with random coefficients.