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NASA NTRS1992

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms and a supervised computational neural network, a dynamic multilayered perception trained with the cascade correlation learning algorithm. Initial clinical results are presented on both normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. However, for a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed.

Hall, Lawrence O.↗

DOE OSTI2026

Learning earthquake ground motions via conditional generative modeling

Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard assessment and infrastructure resilience. Conventional empirical simulations suffer from sparse sensor distribution and geographically localized earthquake locations, while physics-based methods are computationally intensive and require accurate representations of Earth structures and earthquake sources. We propose an artificial intelligence (AI) spectrogram generator, Conditional Generative Modeling for Ground Motion (CGM-GM). CGM-GM leverages earthquake magnitudes and geographic coordinates of earthquakes and sensors as inputs, when postprocessed with phase information, capturing spatially continuous Fourier amplitude spectra (FAS) as well as properties such as P and S arrivals, and waveform durations, without explicit physics constraints. This is achieved through a probabilistic autoencoder that extracts latent distributions in the time-frequency domain and variational sequential models for prior and posterior distributions. We evaluate the performance of CGM-GM using small-magnitude earthquake records from the San Francisco Bay Area, a region with high seismic risks. Here, we report that CGM-GM demonstrates potential for complementing physics-based simulations and non-ergodic empirical ground motion models, as well as shows promise in seismology and beyond.

geophysics↗

DOE OSTI2026

Hofmann Stability Charts Revisited for PIP-II: From Classical Theory to Assumption-Free and ML-Driven Maps

The Hofmann stability chart remains a standard for visualizing parametric resonances in space-charge–dominated linacs, but its use typically relies on non-oscillatory Vlasov dispersion relations with simplifying assumptions (continuous focusing, KV phase space, linear optics, limited transverse–longitudinal coupling). We revisit the chart for the PIP-II linac along three tracks. (1) We reproduce the conventional maps in the (νz/νx, νx/ν0x) plane for relevant εz/εx, providing a validated reference. (2) We remove key assumptions by deriving stability surfaces directly from multi-particle tracking with realistic lattice discreteness, RF defocusing, solenoid/quad optics, and bunched-beam dynamics; local tunes and early-time growth rates are estimated from envelope oscillations and projected to the same coordinates. These assumption-reduced maps recover the canonical stopbands while revealing shifts and broadenings driven by tune modulation, non-KV distributions, and transverse–longitudinal coupling at PIP-II intensities. (3) We train a compact machine-learning surrogate that emulates the growth surface from zero-current optics, tune depression, emittance ratio, bunching factor, and selected lattice descriptors, enabling rapid scans and online working-point selection. We compare the three representations on representative PIP-II sections and discuss implications for commissioning guard bands, resonance avoidance, and routine operations.

Pathak, Abhishek [Fermilab] (ORCID:000000021704208↗

NASA NTRS1984

Utilization of Historic Information in an Optimisation Task

One of the basic components of a discrete model of motor behavior and decision making, which describes tracking and supervisory control in unitary terms, is assumed to be a filtering mechanism which is tied to the representational principles of human memory for time-series information. In a series of experiments subjects used the time-series information with certain significant limitations: there is a range-effect; asymmetric distributions seem to be recognized, but it does not seem to be possible to optimize performance based on skewed distributions. Thus there is a transformation of the displayed data between the perceptual system and representation in memory involving a loss of information. This rules out a number of representational principles for time-series information in memory and fits very well into the framework of a comprehensive discrete model for control of complex systems, modelling continuous control (tracking), discrete responses, supervisory behavior and learning.

Boesser, T.↗

NASA NTRS2020

Bi-Axial Load Testing of a Woven-Webbing Inflatable Space Habitat Restraint Layer Component

This report discusses testing of a bi-axial woven panel consisting of high-strength webbings that represent a portion of the structural restraint layer of an inflatable space structure. Inflatable softgoods vessels are being researched for human space missions as possible habitats, airlocks and tunnel elements. Understanding the complex behavior of the softgoods restraint layer and maturing finite element analysis capabilities to model these structures is critical to their successful implementation. The primary goal of this research is to study the load-up and load distribution in a weave of webbings, before and after the loss of tension in one of the webbings. In addition, a key objective is to evaluate the ability to convert strains measured via photogrammetry using digital image correlation to loads in the weave. The report gives an introduction and objectives for the test program and a description of the test fixture, setup and procedure. This is followed by a section focusing on the test data and associated discussion. To streamline presentation of the data, one focus case is detailed in the main text. (Comprehensive data set can be found in Appendix A for the three remaining test cases.) In addition to the load and time histories for one case, summary charts and tables that incorporate data from all four test cases are provided. The concluding remarks include major findings, lessons learned and recommendations for future work. Appendix B provides complementary full-field strain results when considering the existing woven webbing data as a fabric. These types of results could inform models intended for global representation. Appendix C contains a description from a series of exploratory instrumented hammer tap tests and results on the weave. These tests describe an area of research for the biaxial test fixture beyond the current testing studying the propagation and detection of vibrations in a tensioned weave that has applications in impact and damage detection.

Karen H Lyle↗

NASA NTRS2022

A Generalized Approach to Aircraft Trajectory Prediction via Supervised Deep Learning

As research advances diverse forms and missions of aircraft, the National Airspace System (NAS) will become increasingly crowded, limiting current communications resources to accommodate aviation operations. Ongoing research proposes a paradigm of airspace communications, such that resources are autonomously and dynamically allocated via intelligent agents; this allocation requires accurate representations of the NAS, including the predicted positions of aircraft. State-of-the-art research emphasizes the importance of a hybrid-recurrent framework for trajectory prediction and compares the impact of commonly considered weather data on prediction accuracy. However, current research has been limited in its scope of efforts, frequently utilizing a unique flight route, architecture, set of weather data, and date range. This article considers the challenges of generalizing hybrid-recurrent predictive models for flight trajectories. Results illustrate an increase in error variance when identical models are trained over a generalized set of flights; this may be mitigated with careful tuning of hyperparameters, both in the network structure and optimization algorithms. Even so, an irreducible vertical error was identified, resulting from the complex takeoff and landing procedures which can not be correlated to functions of weather or additional assumptions of aircraft behavior. Finally, the use of a test route indicates that generalized models still do not possess sufficient knowledge for general aircraft predictions, with mean error increases ranging from 70-500%. These results illustrate the need for continued efforts on improving model versatility, as well as potential limitations for spectrum allocation near airports and other centers.

Nathan Schimpf↗

NASA NTRS1993

Two generalizations of Kohonen clustering

The relationship between the sequential hard c-means (SHCM), learning vector quantization (LVQ), and fuzzy c-means (FCM) clustering algorithms is discussed. LVQ and SHCM suffer from several major problems. For example, they depend heavily on initialization. If the initial values of the cluster centers are outside the convex hull of the input data, such algorithms, even if they terminate, may not produce meaningful results in terms of prototypes for cluster representation. This is due in part to the fact that they update only the winning prototype for every input vector. The impact and interaction of these two families with Kohonen's self-organizing feature mapping (SOFM), which is not a clustering method, but which often leads ideas to clustering algorithms is discussed. Then two generalizations of LVQ that are explicitly designed as clustering algorithms are presented; these algorithms are referred to as generalized LVQ = GLVQ; and fuzzy LVQ = FLVQ. Learning rules are derived to optimize an objective function whose goal is to produce 'good clusters'. GLVQ/FLVQ (may) update every node in the clustering net for each input vector. Neither GLVQ nor FLVQ depends upon a choice for the update neighborhood or learning rate distribution - these are taken care of automatically. Segmentation of a gray tone image is used as a typical application of these algorithms to illustrate the performance of GLVQ/FLVQ.

Bezdek, James C.↗

DOE OSTI2025

Masked Symbol Modeling for Demodulation of Oversampled Baseband Communication Signals in Impulsive Noise-Dominated Channels

Recent breakthroughs in natural language processing show that attention mech- anism in Transformer networks, trained via masked-token prediction, enables models to capture the semantic context of the tokens and internalize the grammar of language. While the application of Transformers to communication systems is a burgeoning field, the notion of context within physical waveforms remains under-explored. This paper addresses that gap by re-examining inter-symbol con- tribution (ISC) caused by pulse-shaping overlap. Rather than treating ISC as a nuisance, we view it as a deterministic source of contextual information embedded in oversampled complex baseband signals. We propose Masked Symbol Model- ing (MSM), a framework for the physical (PHY) layer inspired by Bidirectional Encoder Representations from Transformers methodology. In MSM, a subset of symbol-aligned samples is randomly masked, and a Transformer predicts the missing symbol identifiers using the surrounding “in-between” samples. Through this objective, the model learns the latent syntax of complex baseband waveforms. We illustrate MSM’s potential by applying it to the task of demodulating sig- nals corrupted by impulsive noise, where the model infers corrupted segments by leveraging the learned context. Our results suggest a path toward receivers that interpret, rather than merely detect communication signals, opening new avenues for context-aware PHY layer design.

Bedir, Oguz↗

NASA NTRS1985

NASA image-based geological expert system development project for hyperspectral image analysis

The NASA image-based geological expert system was applied to analyze remotely sensed hyperspectral image data. The major objective is for geologists to identify the earth surface mineral properties directly from the airborne and spaceborne imaging spectrometer data. With certain constraints, it is shown that the system can identify correctly different classes of mineral. It has the built-in learning paradigm to enhance the confidence factor of mineral identification. A very powerful natural language system was incorporated as the user-friendly front end, and the concurrent processing efficiency of the frame-based knowledge representation in the hypercube microsupercomputer simulation was tested.

Chiou, W. C., Sr.↗

NASA NTRS1988

Genetic algorithms for adaptive real-time control in space systems

Genetic Algorithms that are used for learning as one way to control the combinational explosion associated with the generation of new rules are discussed. The Genetic Algorithm approach tends to work best when it can be applied to a domain independent knowledge representation. Applications to real time control in space systems are discussed.

Vanderzijp, J.↗

NASA NTRS2005

Development and Testing of Data Mining Algorithms for Earth Observation

The new algorithms developed under this project included a principled procedure for classification of objects, events or circumstances according to a target variable when a very large number of potential predictor variables is available but the number of cases that can be used for training a classifier is relatively small. These "high dimensional" problems require finding a minimal set of variables -called the Markov Blanket-- sufficient for predicting the value of the target variable. An algorithm, the Markov Blanket Fan Search, was developed, implemented and tested on both simulated and real data in conjunction with a graphical model classifier, which was also implemented. Another algorithm developed and implemented in TETRAD IV for time series elaborated on work by C. Granger and N. Swanson, which in turn exploited some of our earlier work. The algorithms in question learn a linear time series model from data. Given such a time series, the simultaneous residual covariances, after factoring out time dependencies, may provide information about causal processes that occur more rapidly than the time series representation allow, so called simultaneous or contemporaneous causal processes. Working with A. Monetta, a graduate student from Italy, we produced the correct statistics for estimating the contemporaneous causal structure from time series data using the TETRAD IV suite of algorithms. Two economists, David Bessler and Kevin Hoover, have independently published applications using TETRAD style algorithms to the same purpose. These implementations and algorithmic developments were separately used in two kinds of studies of climate data: Short time series of geographically proximate climate variables predicting agricultural effects in California, and longer duration climate measurements of temperature teleconnections.

Glymour, Clark↗

DOE OSTI2026

A hybrid Penman-Monteith and machine learning model for simulating evapotranspiration and its components

Integrating physical processes with machine learning has advanced evapotranspiration (ET) simulation, yet most hybrid models fail to partition total ET into its components: soil evaporation (E) and vegetation transpiration (T). This study introduces Residual Neural Network–Penman–Monteith (RNN-PM), a novel hybrid dual-source ET model designed to overcome this limitation. The model synergizes the physically-based Penman–Monteith framework with three specialized residual neural networks trained to estimate key conductance parameters (canopy conductance, soil surface conductance, and aerodynamic conductance). Furthermore this explicit parameterization allows for the direct partitioning of total ET. Validation at National Ecological Observatory Network (NEON) flux sites using high-frequency partitioned E and T shows that RNN-PM reliably reproduces ET and the transpiration fraction (T/ET). For ET, the model achieves an average Kling–Gupta efficiency (KGE) of 0.89 and a root-mean-square error (RMSE) of 0.55 mm/day; for T/ET, the KGE is 0.87 with an RMSE of 0.06. Furthermore, RNN-PM demonstrates robust generalization, accurately simulating ET and its components well beyond the initial training dataset, even under extreme climatic conditions. This study extended the analysis by comparing the RNN-PM model with seven established dual-source ET models. The results indicate that RNN-PM outperforms both conventional machine learning models and purely physical process-based models in simulating ET components in most cases. Among the purely physical process-based dual-source models, those based on surface temperature decomposition showed improved performance as the leaf area index (LAI) decreased when evaluated against high-frequency ET component datasets. In contrast, the performance of conductance-based dual-source models declined with decreasing LAI. Although purely machine learning-based models can produce relatively accurate simulations of ET components, they often exhibit limited generalization capability, an issue that the RNN-PM model effectively overcomes. Ultimately, the RNN-PM model represents a significant advance in simulating ET components, offering a novel and scalable approach for improving the representation of land–atmosphere interactions in Earth system models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

U-Surf: a global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling

High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset to represent the spatial heterogeneity of urban surfaces and their biophysical properties. This deficiency has long obstructed the development of urban-resolving Earth system models (ESMs) and ultra-high-resolution urban climate modeling, over large domains. Here, we present U-Surf, a first-of-its-kind 1 km resolution present-day (circa 2020) global continuous urban surface parameter dataset. Using the urban canopy model (UCM) in the Community Earth System Model as a base model for satisfying dataset requirements, U-Surf leverages the latest advances in remote sensing, machine learning, and cloud computing to provide the most relevant urban surface biophysical parameters, including radiative, morphological, and thermal properties, for UCMs at the facet and canopy level. Generated using a systematically unified workflow, U-Surf ensures internal consistency among key parameters, making it the first globally coherent urban canopy surface dataset. U-Surf significantly improves the representation of the urban land heterogeneity both within and across cities globally; provides essential, high-fidelity surface biophysical constraints to urban-resolving ESMs; enables detailed city-to-city comparisons across the globe; and supports next-generation kilometer-resolution Earth system modeling across scales. U-Surf parameters can be easily converted or adapted to various types of UCMs, such as those embedded in weather and regional climate models, as well as air quality models. The fundamental urban surface constraints provided by U-Surf can also be used as features for machine learning models and can have other broad-scale applications for socioeconomic, public health, and urban planning contexts. We expect U-Surf to advance the research frontier of urban system science, climate-sensitive urban design, and coupled human–Earth systems in the future. The dataset is publicly available at https://doi.org/10.5281/zenodo.11247598 (Cheng et al., 2024).

Cheng, Yifan [Univ. of Illinois at Urbana-Champaig↗

NASA NTRS2007

Sensing Super-Position: Human Sensing Beyond the Visual Spectrum

The coming decade of fast, cheap and miniaturized electronics and sensory devices opens new pathways for the development of sophisticated equipment to overcome limitations of the human senses. This paper addresses the technical feasibility of augmenting human vision through Sensing Super-position by mixing natural Human sensing. The current implementation of the device translates visual and other passive or active sensory instruments into sounds, which become relevant when the visual resolution is insufficient for very difficult and particular sensing tasks. A successful Sensing Super-position meets many human and pilot vehicle system requirements. The system can be further developed into cheap, portable, and low power taking into account the limited capabilities of the human user as well as the typical characteristics of his dynamic environment. The system operates in real time, giving the desired information for the particular augmented sensing tasks. The Sensing Super-position device increases the image resolution perception and is obtained via an auditory representation as well as the visual representation. Auditory mapping is performed to distribute an image in time. The three-dimensional spatial brightness and multi-spectral maps of a sensed image are processed using real-time image processing techniques (e.g. histogram normalization) and transformed into a two-dimensional map of an audio signal as a function of frequency and time. This paper details the approach of developing Sensing Super-position systems as a way to augment the human vision system by exploiting the capabilities of Lie human hearing system as an additional neural input. The human hearing system is capable of learning to process and interpret extremely complicated and rapidly changing auditory patterns. The known capabilities of the human hearing system to learn and understand complicated auditory patterns provided the basic motivation for developing an image-to-sound mapping system. The human brain is superior to most existing computer systems in rapidly extracting relevant information from blurred, noisy, and redundant images. From a theoretical viewpoint, this means that the available bandwidth is not exploited in an optimal way. While image-processing techniques can manipulate, condense and focus the information (e.g., Fourier Transforms), keeping the mapping as direct and simple as possible might also reduce the risk of accidentally filtering out important clues. After all, especially a perfect non-redundant sound representation is prone to loss of relevant information in the non-perfect human hearing system. Also, a complicated non-redundant image-to-sound mapping may well be far more difficult to learn and comprehend than a straightforward mapping, while the mapping system would increase in complexity and cost. This work will demonstrate some basic information processing for optimal information capture for headmounted systems.

Maluf, David A.↗

NASA NTRS2011

Feature Acquisition with Imbalanced Training Data

This work considers cost-sensitive feature acquisition that attempts to classify a candidate datapoint from incomplete information. In this task, an agent acquires features of the datapoint using one or more costly diagnostic tests, and eventually ascribes a classification label. A cost function describes both the penalties for feature acquisition, as well as misclassification errors. A common solution is a Cost Sensitive Decision Tree (CSDT), a branching sequence of tests with features acquired at interior decision points and class assignment at the leaves. CSDT's can incorporate a wide range of diagnostic tests and can reflect arbitrary cost structures. They are particularly useful for online applications due to their low computational overhead. In this innovation, CSDT's are applied to cost-sensitive feature acquisition where the goal is to recognize very rare or unique phenomena in real time. Example applications from this domain include four areas. In stream processing, one seeks unique events in a real time data stream that is too large to store. In fault protection, a system must adapt quickly to react to anticipated errors by triggering repair activities or follow- up diagnostics. With real-time sensor networks, one seeks to classify unique, new events as they occur. With observational sciences, a new generation of instrumentation seeks unique events through online analysis of large observational datasets. This work presents a solution based on transfer learning principles that permits principled CSDT learning while exploiting any prior knowledge of the designer to correct both between-class and withinclass imbalance. Training examples are adaptively reweighted based on a decomposition of the data attributes. The result is a new, nonparametric representation that matches the anticipated attribute distribution for the target events.

Thompson, David R.↗

NASA NTRSDate not supplied

Using Machine Learning to Classify Earth Science Articles

Developing a machine learning pipeline to automatic the classification of research article topics, where topics are geophysical descriptors. The resulting classified documents will be added to the Usage-Based Discovery graph database, along with corresponding NASA Earth Science datasets, with the intent of linking the NASA data product with uses of it that can be searched by topic. The project will aid in the following goals (1) creating a wider representation of how and how much NASA Earth Science data products are used, (2) identifying when NASA data products have been used even when they are not clearly cited, and (3) providing the UBD search tool end-user with set of recommended datasets based on a given topic query.

Machine Learning↗

NASA NTRSDate not supplied

Celestial Mapping System and Digital Lunar Library Initiative

We are preparing to create an interactive, global 3D lunar environment with integrated dataset and AI/ML tools to provide unique value to mission planners, scientists and the entire lunar community. This lunar environment will be based on NASA Ames Celestial Mapping System (CMS) [1] and Digital Lunar Library (DLL) Initiative. CMS provides a 3D virtual Lunar Globe with extensive user friendly tool sets, that include high resolution terrain visualization, elevation profiles, measurement kits, slope analysis, path optimization, line of sight analysis, equipment planning and placement tools and many other functionalities [1]. It has a thick client with less overhead to access hardware resources. This allows features such as terrain profiling and distance calculations to be performed on the client and on the fly. The application is developed to provide situational and domain awareness on the Lunar surface, planning capabilities for equipment placements and traverse path optimization. As data becomes available, CMS has the capabilities to integrate data sets that change dynamically in real-time, which will be useful for monitoring satellites and remotely-sensed data on the Lunar surface. CMS supports importing synthetic features in a variety of 3D, 2D, vector and raster formats. In the future, these capabilities will be enhanced by incorporating AI/ML tools and a plug-in architecture to enable customization by the user groups. With the help of DLL we will be able to : 1) Amplify the value of lunar information with AI-powered data enhancements 2) Acquire and integrate lunar data with AI-assisted georectification and homogenization 3) Analyze lunar data with advanced 3D visualization, intelligent search-by-example 4) Apply lunar data insights to specific use cases with an open plug-in architecture. The CMS-DLL initiative will have several potential use cases for NASA and the lunar community in general, including subsurface lava tube visualization and analysis, soil analysis, in-situ lunar resource visualization and representation on 3D globe, and data analytics for utilization. REFERENCES: [1] https://celestial.arc.nasa.gov/

3D Globe↗

DOE OSTI2024

End-to-end codesign of Hessian-aware quantized neural networks for FPGAs

Here, we develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) hardware. Our approach leverages Hessian-aware quantization of NNs, the Quantized Open Neural Network Exchange intermediate representation, and the hls4ml tool flow for transpiling NNs into FPGA firmware. This makes efficient NN implementations in hardware accessible to nonexperts in a single open sourced workflow that can be deployed for real-time machine-learning applications in a wide range of scientific and industrial settings. We demonstrate the workflow in a particle physics application involving trigger decisions that must operate at the 40-MHz collision rate of the CERN Large Hadron Collider (LHC). Given the high collision rate, all data processing must be implemented on FPGA hardware within the strict area and latency requirements. Based on these constraints, we implement an optimized mixed-precision NN classifier for high-momentum particle jets in simulated LHC proton-proton collisions.

47 OTHER INSTRUMENTATION↗