Search NASA⌕ Search

SEARCH · Search NASA

Results for “interpretable models”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Symbolic diagnostics to interpret and analyze neural network models

Embedded machine-learned models (EMLMs) have the promise to improve the predictive accuracy of engineering simulators in environments of national interest. EMLMs often comprise complex input-output maps (e.g., neural networks), which make them unamenable to rigorous analysis and generally difficult to interpret. In the face of decades of theory, this lack of interpretability is a significant barrier to building confidence in these models. This work outlines an approach to interpret EMLMs using sparse polynomial regression for comparison with theoretical understanding. To do so, we build on the concept of Locally Interpretable Model-agnostic Explanations (LIME) using physics-informed clustering, prototype selection, and library construction. While general, we demonstrate our method on tensor-basis neural networks used in Reynolds-Averaged Navier-Stokes simulations of hypersonic fluid flows. Results are presented for a simulated toy model and for direct numerical simulations (DNS) of turbulent flows over a flat plate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Explaining System-Level Prognostics with Established Machine Learning Methods

System-level prognostics is crucial for ensuring reliability and enabling predictive maintenance in complex systems with interconnected components. This study presents a framework that integrates data-driven methods to predict the remaining useful life (RUL) of a subsystem under multiple and concurrent faults within a nuclear power plant system with explainable artificial intelligence (XAI). A nuclear power plant (NPP) operation was simulated to model the degradation behavior of NPP components, and four machine learning models—Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory (LSTM)—were evaluated for prognostics with a novel system RUL parameter. The LSTM model demonstrated potential superior repeatability, while SHAP (SHapley Additive exPlanations) for explainability provided consistent and trustworthy global explanations. In contrast, LIME (Local Interpretable Model-agnostic Explanations) offered localized interpretability but showed reduced stability for sequential data. Key findings include the interplay between component-level degradation and system-wide performance, with LSTM effectively capturing these dynamics through sequence-level predictions. The XAI techniques enhanced transparency by identifying critical features influencing model predictions and aligning with domain knowledge. Furthermore, this framework has significant implications for improving trust and understanding in predictive maintenance, particularly in safety-critical industries like nuclear energy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling

Recent advances in differentiable modeling, a genre of physics-informed machine learning that trains neural networks (NNs) together with process-based equations, have shown promise in enhancing hydrological models' accuracy, interpretability, and knowledge-discovery potential. Current differentiable models are efficient for NN-based parameter regionalization, but the simple explicit numerical schemes paired with sequential calculations (operator splitting) can incur numerical errors whose impacts on models' representation power and learned parameters are not clear. Implicit schemes, however, cannot rely on automatic differentiation to calculate gradients due to potential issues of gradient vanishing and memory demand. Here we propose a “discretize-then-optimize” adjoint method to enable differentiable implicit numerical schemes for the first time for large-scale hydrological modeling. The adjoint model demonstrates comprehensively improved performance, with Kling–Gupta efficiency coefficients, peak-flow and low-flow metrics, and evapotranspiration that moderately surpass the already-competitive explicit model. Therefore, the previous sequential-calculation approach had a detrimental impact on the model's ability to represent hydrological dynamics. Furthermore, with a structural update that describes capillary rise, the adjoint model can better describe baseflow in arid regions and also produce low flows that outperform even pure machine learning methods such as long short-term memory networks. The adjoint model rectified some parameter distortions but did not alter spatial parameter distributions, demonstrating the robustness of regionalized parameterization. Despite higher computational expenses and modest improvements, the adjoint model's success removes the barrier for complex implicit schemes to enrich differentiable modeling in hydrology.

58 GEOSCIENCES↗
NASA NTRS1975

Radiation from a homogeneous sphere mounted on a waveguide aperture

The results of an experimental and theoretical study of the effects of placing a dielectric sphere over the aperture of a circular waveguide are presented. A dielectric sphere excited by a Huygens' source is used as the theoretical model to interpret the experimental results. Calculations based on this model show good overall agreement with experimental patterns and directivity measurements. It is concluded that dielectric loaded antennas such as the dielectric sphere excited by a waveguide can produce directivities in excess of that obtained by a uniformly illuminated aperture of the same cross section, particularly for dielectric objects with dimensions of two wavelengths or less. Measurements of the coupling between these antennas show that they, in general, are not as closely coupled as plain apertures without dielectric object loading.

Croswell, W. F.↗
NASA NTRS1981

Shaded computer graphic techniques for visualizing and interpreting analytic fluid flow models

Mathematical models which predict the behavior of fluid flow in different experiments are simulated using digital computers. The simulations predict values of parameters of the fluid flow (pressure, temperature and velocity vector) at many points in the fluid. Visualization of the spatial variation in the value of these parameters is important to comprehend and check the data generated, to identify the regions of interest in the flow, and for effectively communicating information about the flow to others. The state of the art imaging techniques developed in the field of three dimensional shaded computer graphics is applied to visualization of fluid flow. Use of an imaging technique known as 'SCAN' for visualizing fluid flow, is studied and the results are presented.

Parke, F. I.↗
NASA NTRSDate not supplied

Capturing Complex Multivariate Time Series Interactions to Detect High-Risk Adverse Events During Flight

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗
NASA NTRSDate not supplied

Capturing Multivariate Time Series Interactions to Detect High‑Risk Instability During Approach

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗
DOE OSTI2026

A Data-Driven Method for Modeling Creep-Fatigue Stress- Strain Behavior Using Neural ODEs

In this paper, we introduce a data-driven machine learning approach for modeling one-dimensional stress–strain behavior under cyclic loading, utilizing experimental data from the nickel-based Alloy 617. The study employs uniaxial creep–fatigue test data acquired under various loading histories and compares two distinct neural network-based ODE models. The first model, known as the black-box model, comprehensively describes the strain–stress relationship using a Neural ODE equation. To interpret this black-box model, we apply the Sparse Identification of Nonlinear Dynamical Systems (SINDy) technique, transforming the black-box model into an equation-based model using symbolic regression. The second model, the Neural flow rule model, incorporates Hooke’s Law for the linear elastic component, with the nonlinear part characterized by a Neural ODE. Both models are trained with experimental data to accurately reflect the observed stress–strain behavior. We conduct a detailed comparison with the standard Chaboche model, which includes three back stresses. Our results demonstrate that the neural network-based ODE models precisely capture the experimental creep–fatigue mechanical behavior, exceeding the standard Chaboche model’s accuracy. Furthermore, an interpretable model derived from the black-box neural ODE model through symbolic regression achieves accuracy comparable to the Chaboche model, enhancing its interpretability. The results highlight the potential of neural network-based ODE models to depict complex creep–fatigue behavior, eliminating the necessity for experts to define a specific, material-focused model form.

creep-fatigue↗
NASA NTRS1993

Application of fuzzy set and Dempster-Shafer theory to organic geochemistry interpretation

An application of fuzzy sets and Dempster Shafter Theory (DST) in modeling the interpretational process of organic geochemistry data for predicting the level of maturities of oil and source rock samples is presented. This was accomplished by (1) representing linguistic imprecision and imprecision associated with experience by a fuzzy set theory, (2) capturing the probabilistic nature of imperfect evidences by a DST, and (3) combining multiple evidences by utilizing John Yen's generalized Dempster-Shafter Theory (GDST), which allows DST to deal with fuzzy information. The current prototype provides collective beliefs on the predicted levels of maturity by combining multiple evidences through GDST's rule of combination.

Kim, C. S.↗
NASA NTRS1992

Modeling Characteristics Of Surfaces For Radar Polarimetry

Paper reviews mathematical models of polarimetric radar backscattering characteristics of various types of terrain; forests, grasslands, and lava fields. Represents approach to imaging radar polarimetry in which one accumulates models predicting realistic polarization signatures and represent distinct scattering processes, without attempting full vector solutions of Maxwell's equations in all cases. Idea to develop ability to invert models to identify unknown terrain depicted in polarimetric radar images. Describes models, major scattering characteristics predicted by models, and interpretation of characteristics in terms of dominant scattering mechanisms. Models predict realistic polarization signatures.

Van Zyl, Jakob J.↗
NASA NTRS2007

Mesospheric Water Vapor Retrieved from SABER/TIMED Measurements

The SABER instrument on board the TIMED satellite is a limb scanning infrared radiometer designed to measure temperature and minor constituent vertical profiles and energetics parameters in the mesosphere and lower thermosphere (MLT) The H2O concentrations are retrieved from 6.3 micron band radiances. The interpretation of this radiance requires developing a non-LTE H2O model that includes energy exchange processes with the system of O3 and O2 vibrational levels populated at the daytime through a number of photoabsorption and photodissociation processes. We developed a research model base on an extended H2O non-LTE model of Manuilova coupled with the novel model of the electronic kinetics of the O2 and O3 photolysis products suggested by Yankosvky and Manuilova. The performed study of this model helped u to develop and test an optimized operational model for interpretation of SABER 6.3 micron band radiances. The sensitivity of retrievals to the parameters of the model is discussed. The H2O retrievals are compared to other measurements for different seasons and locations.

Feofilov, Arte, G.↗
DOE OSTI2025

Experimental Soil Warming Impacts Soil Moisture and Plant Water Stress and Thereby Ecosystem Carbon Dynamics

Experimental soil heating experiments have found a consistent increase in soil-surface CO 2 emissions ( F s ), but inconsistent soil organic carbon (SOC) responses. Interpretation of heating effects is complicated by spatial heterogeneity and soil moisture, nitrogen availability, and microbial and plant responses. Here we applied a mechanistic ecosystem model to interpret heating impacts on a California forest subjected to 1 m deep, 4°C heating. The model accurately simulated control-plot CO 2 fluxes, SOC stocks, fine root biomass, soil moisture, and soil temperature, and the observed increases in F s and decreases in fine root biomass. We show that a complex suite of interactions can lead to a consistent increase in F s (~17%) over the 5-year study period, with very small changes in SOC stocks (<1%). Modeled increases in leaf water stress from soil drying reduced GPP and NPP. The resulting reduction in leaf and fine root allocation increased fine root litter inputs to the soil and reduced root exudation. Soil heating led to about a 50% larger increase in root autotrophic respiration than in heterotrophic respiration, with the heating effect on both these fluxes decreasing over the simulation period. Increased heterotrophic respiration led to increased soil N availability and plant N uptake. These heating responses are mechanistically linked, of magnitudes that can affect ecosystem dynamics, and long-term observations of them are rarely made. Therefore, we conclude that a coupled observational and mechanistic modeling framework is needed to interpret manipulation experiments, and to improve projections of climate change impacts on terrestrial ecosystem carbon dynamics.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

Bayesian Inference in Satellite Gravity Inversion

To solve a geophysical inverse problem means applying measurements to determine the parameters of the selected model. The inverse problem is formulated as the Bayesian inference. The Gaussian probability density functions are applied in the Bayes's equation. The CHAMP satellite gravity data are determined at the altitude of 400 kilometer altitude over the South part of the Pannonian basin. The model of interpretation is the right vertical cylinder. The parameters of the model are obtained from the minimum problem solved by the Simplex method.

Kis, K. I.↗
NASA NTRSDate not supplied

Latest Results from NASA Ames’ COSmIC and Optical Constants Facility (OCF): Determining Optical Constants for Titan, Pluto and Protoplanetary Disks Applications

The NASA Ames COsmic SImulation Chamber (COSmIC) is a unique experimental facility that can be used, among many applications, to produce solid particles from gas phase molecular precursors at low temperature (150 K) using a plasma discharge to induce the chemistry in the stream of a free jet expansion. The choice of the initial gas mixture used to produce the solid sample allows the simulation of either cold planetary atmospheres like Titan or Pluto (with N2/CH4-based initial mixtures), or circumstellar environments (with Ar/CxHy-based initial mixtures). The Ames Optical Constants Facility (OCF) allows the determination of optical constants covering a broad wavelength range with high spectral resolution for solid materials, analogs of organic refractory materials formed in planetary and astrophysical environments. The core of the OCF is a Fourier transform infrared (FTIR) spectrometer that allows the continuous characterization of solid samples in the visible to far-infrared (FIR) range (0.59–200 μm, 16,950–50 cm−1). Modeling of the laboratory measurements conducted with the OCF allows the determination of accurate optical constants, n and k, over the full vis-FIR range. Here we present the latest results of two studies that combined (1) experiments performed with COSmIC to produce analogs of aerosols forming in Titan’s atmosphere and analogs of cosmic grains forming in circumstellar envelops, and (2) the characterization of these analogs with the OCF to provide the real and imaginary parts of their refractive indices, n + ik, to the community, from the visible to the FIR. These optical constants can be used as critical input parameters in radiative transfer, atmospheric and reflectance models to interpret observational data of, e.g., Titan’s atmosphere and protoplanetary disks. Providing optical constants for various materials of different compositions allows to explore a broad range of composition by simulating mixtures of materials. We also present a new project to produce analogs of Pluto’s atmospheric aerosols with COSmIC and determine their optical constants with OCF, to be used in reflectance spectra models for the interpretation of New Horizons observations of Lowell Regio, Sputnik Planitia and Cthulhu.

COsmic SImulation Chamber (COSmIC)↗
DOE OSTI2026

Role of Neutrals Versus Transport in Determining the Pedestal Density Structure: Final Technical Report

In fusion devices the plasma density plays a crucial role in determining the fusion reaction rate and has a direct impact on the fusion gain of a given device. This density is in general regulated by the particle sources and transport near the plasma edge, which give rise to an edge density pedestal. When predicting the performance of future devices, this density pedestal is often prescribed, rather than predicted, due to a lack of models which allow confident extrapolation. This project aims to advance these models through the focused validation of theoretical models related to the transport of fueling neutral particles, and through interpretive transport modeling in present day fusion plasmas, in which the penetration of neutrals is altered to better simulate future reactor-like conditions. Achievements in theory and model validation under this project have advanced our understanding of how much of the edge density profile is set by transport versus direct ionization, enabling interesting projections to future burning plasma devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1992

Event-driven simulation in SELMON: An overview of EDSE

EDSE (event-driven simulation engine), a model-based event-driven simulator implemented for SELMON, a tool for sensor selection and anomaly detection in real-time monitoring is described. The simulator is used in conjunction with a causal model to predict future behavior of the model from observed data. The behavior of the causal model is interpreted as equivalent to the behavior of the physical system being modeled. An overview of the functionality of the simulator and the model-based event-driven simulation paradigm on which it is based is provided. Included are high-level descriptions of the following key properties: event consumption and event creation, iterative simulation, synchronization and filtering of monitoring data from the physical system. Finally, how EDSE stands with respect to the relevant open issues of discrete-event and model-based simulation is discussed.

Rouquette, Nicolas F.↗
DOE OSTI2026

Assessing Metal Ion Assignment Accuracy in Protein Data Bank Models via Elemental Spectroscopy

Accurate representation of metal ions in macromolecular structures is critical for chemical interpretation, computational modeling, and machine-learning methods that rely on Protein Data Bank (PDB) entries. However, the elemental identity of metals modeled in crystallographic structures is often inferred indirectly and rarely validated experimentally. Here, we combine Particle Induced X-ray Emission (PIXE) and X-ray Fluorescence Spectroscopy (XRFS) to determine the elemental composition of protein samples used to generate 70 deposited metalloprotein crystal structures. By analyzing the original protein material employed for crystallization, but before the addition of crystallization buffer solutions, we assess whether the modeled metal ions in deposited structures are consistent with experimentally detectable elemental content. We find that in a majority of cases, the metals modeled in the corresponding PDB entries are inconsistent with the metals present in the protein samples before crystallization, or that additional metals are present but not represented in the structural models. Spectroscopic results were integrated with automated crystallographic validation metrics, including real-space Z-difference (RSZD) analysis and systematic rerefinement, to evaluate atomic-number mismatch at metal sites. PIXE and XRFS show strong agreement for dominant elemental signals and provide complementary, scalable approaches for identifying suspect metal assignments. This work does not address physiological or functional metalation but instead highlights a widespread data integrity issue in deposited macromolecular structures, PDB-wide. These results establish an experimentally corroborated link between elemental identity and crystallographic validation metrics, enabling the large-scale detection of chemically inconsistent annotations in structural databases used for computational modeling and machine learning.

Crystallization↗