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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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Advanced Error Modeling for Prompt Yield Estimation [Slides]
Multi-Phenomenology Explosion Monitoring (MultiPEM) Toolbox provides a capability for post-detonation characterization focusing on yield estimation; Synthesize information from multiple sensor types; Leverage historical databases to inform statistical models; Allows for rapid or comprehensive analysis; Implemented in R code.
Optimizing Prediction Error for Time-dependent Solar Radiation Modeling
Numerical weather forecasting models and statistical methods have found wide use to help power companies estimate renewable output, but better methods are needed, particularly for extended forecasts. Machine learning approaches have been used here as well, but so far a major limitation is the ability to also predict the corresponding uncertainty in a forecast. Here we show that both can be done and demonstrate this using a long-term-short memory neural network where the difference between predicted and ground truth data are used to train a model for the corresponding forecast uncertainties.
Practical VVUQ–The Interplay of Model Form, Uncertainty Quantification and Error in Computational Predictions from a Validation Assessment Perspective [Slides]
Abstract not provided.
Error-in-variables modelling for operator learning
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Complex Couplings and Simple Springs: Analysis of Model-Form Error for Highly Nonlinear Oscillatory Systems
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Estimating model-form errors using random field models
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A Goal-oriented Approach to Model Form Error for Constitutive Models.
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Demonstration of Mølmer–Sørensen Gates Robust to +/-10 kHz Motional Frequency Error
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Estimating the Error in Solutions to Stochastic Inverse Problems When Using Machine Learning Surrogates
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Probing logical error models with gate set tomography
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scientific ML, AI, and UQ, with applications to quantum error mitigation
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Model-form Error Correction using Universal Differential Equations for an Agent-Based Model of Infectious Disease
This report demonstrates universal differential equations (UDEs) as an approach to bridge the gap between ordinary differential equations (ODE) models and agent-based models (ABMs). Using UDE models as surrogates for ABMs allows us to preserve the foundational ODE that represents global disease dynamics while coupling it with a neural network model to approximate functions for the local behaviors of the ABM.
Error Mitigation, Optimization, and Extrapolation on a Trapped Ion Testbed
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Nonlinear Forces and Omitted Masses: Mass-Spring-Damper Models and Their Model-Form Errors
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APS March Meeting 2024: "Error attribution for singlet-triplet qubits through randomized benchmarking in the presence of temporally correlated noise"
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Validation Error Extrapolation: Exploration of Concepts and Hypersonic Aerothermodynamics Application
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Performance Benchmark for Error Recovery and Experimental Coverage [Slides]
Abstract not provided.