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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Multilevel Logistic Regression with Random Slope for Community Annoyance Survey Data

This paper documents recent dose-response modeling work at NASA in anticipation of follow-on work by a contactor. Specifically, this paper compares the results of a Bayesian MLR model with a fixed slope to one with a random slope using WSPR and QSF18 data. Previously reported dose-response modeling efforts of WSPR and QSF18 data have used a MLR model with a fixed slope term. A random slope may more accurately depict the dose-response relationship of individuals in the efforts to produce a population summary dose-response curve. Results of a fixed versus random slope model with WSPR and QSF18 data indicate minimal difference between the modeling methods. The simpler fixed slope model is preferable for these data, but these results do not preclude consideration of a random slope term in modeling efforts of future X-59 community test data.

dose-response↗
NASA NTRSDate not supplied

Differential Equation Approximation Using Gradient-Boosted Quantile Regression

The operation of cyber-physical-human (CPH) systems is subject to various epistemic and aleatory uncertainties. Overall trustworthiness of CPH systems relies on the trustworthiness of its components and their interactions. It is important that computational models comprising the cyber component of CPH provide predictions accompanied by a measure of confidence in model outcomes. Uncertainty quantification (UQ) and propagation are especially important in safety critical CPH systems. Gradient-boosted trees is a modeling approach capable both of learning the dynamics of a system and performing UQ. In this paper, we devise a method for using gradient boosting to learn the dynamics of a second order differential equation and estimate uncertainty at the same time. We do this by creating a custom loss function that trains the model to approximate the second derivative of a noisy time series, and to penalize based on a parameter that corresponds to the desired quantile. The resulting gradient boosting model can simulate stochastic trajectories of the system given a single starting point, that is, it can estimate both the expected trajectory and its uncertainty. We show that the uncertainty estimation is well calibrated and that the model can learn the dynamics even in the presence of noise. We demonstrate the approach on a simple cartpole system.

Autonomous systems↗