Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials
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36 MATERIALS SCIENCE↗
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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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Learning stochastic dynamics with statistics-informed neural network
We introduce a machine-learning framework named statistics-informed neural network (SINN) for learning stochastic dynamics from data. This new architecture was theoretically inspired by a universal approximation theorem for stochastic systems, which we introduce in this paper, and the projection-operator formalism for stochastic modeling. Here, we devise mechanisms for training the neural network model to reproduce the correct statistical behavior of a target stochastic process. Numerical simulation results demonstrate that a well-trained SINN can reliably approximate both Markovian and non-Markovian stochastic dynamics. We demonstrate the applicability of SINN to coarse-graining problems and the modeling of transition dynamics. Furthermore, we show that the obtained reduced-order model can be trained on temporally coarse-grained data and hence is well suited for rare-event simulations.
97 MATHEMATICS AND COMPUTING↗
Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach
The escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and profitability of CSP depend on the Direct Normal Irradiance (DNI) component of solar radiation; hence, accurate DNI forecasting can help optimize CSP plants’ operations and performance. The unpredictable nature of weather phenomena, particularly cloud cover, introduces uncertainty into DNI projections. Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. This research addresses the challenge by presenting a novel approach that formulates DNI prediction as a multi-class classification problem, departing from conventional regression-based methods. The primary objective of this classification framework is to identify optimal periods aligning with specific operational thresholds for CSP plants, contributing to enhanced dispatch optimization strategies. We model the DNI classification problem using four advanced deep neural networks – rectified linear unit (ReLU) networks, 1D residual networks (ResNets), bidirectional long short-term memory (BiLSTM) networks, and transformers – achieving accuracies up to 93.5% without requiring meteorological parameters.
14 SOLAR ENERGY↗
Einstein–Rosen waves and the Geroch group
Under the action of the Geroch group, the Minkowski metric can be transformed into any vacuum metric with two commuting Killing vectors. In principle, this reduces the problem of deriving vacuum metrics with two commuting Killing vectors to pure algebra. In this short note, we use these facts to give a purely algebraic derivation of the Einstein-Rosen metric, which describes a cylindrical gravitational wave. Furthermore, our derivation has a straightforward extension to gravitational pulse waves.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Novel symmetry-preserving neural network model for phylogenetic inference
Abstract Motivation Scientists world-wide are putting together massive efforts to understand how the biodiversity that we see on Earth evolved from single-cell organisms at the origin of life and this diversification process is represented through the Tree of Life. Low sampling rates and high heterogeneity in the rate of evolution across sites and lineages produce a phenomenon denoted “long branch attraction” (LBA) in which long nonsister lineages are estimated to be sisters regardless of their true evolutionary relationship. LBA has been a pervasive problem in phylogenetic inference affecting different types of methodologies from distance-based to likelihood-based. Results Here, we present a novel neural network model that outperforms standard phylogenetic methods and other neural network implementations under LBA settings. Furthermore, unlike existing neural network models in phylogenetics, our model naturally accounts for the tree isomorphisms via permutation invariant functions which ultimately result in lower memory and allows the seamless extension to larger trees. Availability and implementation We implement our novel theory on an open-source publicly available GitHub repository: https://github.com/crsl4/nn-phylogenetics.
59 BASIC BIOLOGICAL SCIENCES↗
Large-scale deep learning for metastasis detection in pathology reports
Objectives No existing algorithm can reliably identify metastasis from pathology reports across multiple cancer types and the entire US population. In this study, we develop a deep learning model that automatically detects patients with metastatic cancer by using pathology reports from many laboratories and of multiple cancer types. Materials and Methods We use 60 471 unstructured pathology reports from 4 Surveillance, Epidemiology, and End Results (SEER) registries. The reports were coded into 1 of 3 labels: metastasis negative, metastases positive, or metastasis undetermined. We utilize a task-specific deep neural network trained from scratch and compare its performance with a widely used large language model (LLM). Results Our deep learning architecture trained on task-specific data outperforms a general-purpose LLM, with a recall of 0.894 compared to 0.824. We quantified model uncertainty and used it to defer reports for human review. We found that retaining 72.9% of reports increased recall from 0.894 to 0.969. Discussion A smaller deep learning architecture trained on task-specific data outperforms a general LLM. Equally critical to model performance is the incorporation of uncertainty quantification, achieved here through an abstention mechanism. Conclusions This study’s finding demonstrate the feasibility of developing algorithms to automatically identify metastatic cancer cases from unstructured pathology reports.
machine learning↗
Solving classical inference problems on modern machine-learning platforms [Slides]
The outline for this presentation includes: Classical inference (i.e., learning) problems in dynamical systems (through examples), Adjoint state methods, Machine learning for time series data, and Examples characterizing three classes of inference/learning problems.
97 MATHEMATICS AND COMPUTING↗
Short-term hemodynamic performance of the mitral Carpentier-Edwards PERIMOUNT pericardial valve. Carpentier-Edwards PERIMOUNT Investigators
BACKGROUND: Although long-term durability data exist, little data are available concerning the hemodynamic performance of the Carpentier-Edwards PERIMOUNT pericardial valve in the mitral position. METHODS: Sixty-nine patients who were implanted with mitral PERIMOUNT valves at seven international centers between January 1996 and February 1997 consented to participate in a short-term echocardiography follow-up. Echocardiographs were collected at a mean of 600+/-133 days after implantation (range, 110 to 889 days); all underwent blinded core lab analysis. RESULTS: At follow-up, peak gradients were 9.09+/-3.43 mm Hg (mean, 4.36+/-1.79 mm Hg) and varied inversely with valve size (p < 0.05). The effective orifice areas were 2.5+/-0.6 cm2 and tended to increase with valve size (p = 0.08). Trace mitral regurgitation (MR) was common (n = 48), 9 patients had mild MR, 1 had moderate MR, none had severe MR. All MR was central (n = 55) or indeterminate (n = 3). No paravalvular leaks were observed. Mitral regurgitation flow areas were 3.4+/-2.8 cm2 and were without significant volumes. CONCLUSIONS: In this multicenter study, these mitral valves are associated with trace, although physiologically insignificant, central MR. Despite known echocardiographic limitations, the PERIMOUNT mitral valves exhibit similar hemodynamics to other prosthetic valves.
Clinical Trial↗
Prognostic significance of electrical alternans versus signal averaged electrocardiography in predicting the outcome of electrophysiological testing and arrhythmia-free survival
OBJECTIVE: To investigate the accuracy of signal averaged electrocardiography (SAECG) and measurement of microvolt level T wave alternans as predictors of susceptibility to ventricular arrhythmias. DESIGN: Analysis of new data from a previously published prospective investigation. SETTING: Electrophysiology laboratory of a major referral hospital. PATIENTS AND INTERVENTIONS: 43 patients, not on class I or class III antiarrhythmic drug treatment, undergoing invasive electrophysiological testing had SAECG and T wave alternans measurements. The SAECG was considered positive in the presence of one (SAECG-I) or two (SAECG-II) of three standard criteria. T wave alternans was considered positive if the alternans ratio exceeded 3.0. MAIN OUTCOME MEASURES: Inducibility of sustained ventricular tachycardia or fibrillation during electrophysiological testing, and 20 month arrhythmia-free survival. RESULTS: The accuracy of T wave alternans in predicting the outcome of electrophysiological testing was 84% (p < 0.0001). Neither SAECG-I (accuracy 60%; p < 0.29) nor SAECG-II (accuracy 71%; p < 0.10) was a statistically significant predictor of electrophysiological testing. SAECG, T wave alternans, electrophysiological testing, and follow up data were available in 36 patients while not on class I or III antiarrhythmic agents. The accuracy of T wave alternans in predicting the outcome of arrhythmia-free survival was 86% (p < 0.030). Neither SAECG-I (accuracy 65%; p < 0.21) nor SAECG-II (accuracy 71%; p < 0.48) was a statistically significant predictor of arrhythmia-free survival. CONCLUSIONS: T wave alternans was a highly significant predictor of the outcome of electrophysiological testing and arrhythmia-free survival, while SAECG was not a statistically significant predictor. Although these results need to be confirmed in prospective clinical studies, they suggest that T wave alternans may serve as a non-invasive probe for screening high risk populations for malignant ventricular arrhythmias.
Non-NASA Center↗
Antarctic isolation: immune and viral studies
Stressful environmental conditions are a major determinant of immune reactivity. This effect is pronounced in Australian National Antarctic Research Expedition populations exposed to prolonged periods of isolation in the Antarctic. Alterations of T cell function, including depression of cutaneous delayed-type hypersensitivity responses and a peak 48.9% reduction of T cell proliferation to the mitogen phytohaemagglutinin, were documented during a 9-month period of isolation. T cell dysfunction was mediated by changes within the peripheral blood mononuclear cell compartment, including a paradoxical atypical monocytosis associated with altered production of inflammatory cytokines. There was a striking reduction in the production by peripheral blood mononuclear cells of the predominant pro-inflammatory monokine TNF-alpha and changes were also detected in the production of IL-1, IL-2, IL-6, IL-1ra and IL-10. Prolonged Antarctic isolation is also associated with altered latent herpesvirus homeostasis, including increased herpesvirus shedding and expansion of the polyclonal latent Epstein-Barr virus-infected B cell population. These findings have important long-term health implications.
NASA Discipline Environmental Health↗
Neuromorphic processing for next generation automotive control and diagnostics
This paper describes intra-layer architecture of a neuroprocessor organized in a SIMD configuration and the two challenging applications, misfire detection and engine idle speed control, that had served as the focus of this effort.
neuroprocessors idle speed control misfire detecti↗
Quantum Analog Computing
Quantum analog computing is based upon similarity between mathematical formalism of quantum mechanics and phenomena to be computed. It exploits a dynamical convergence of several competing phenomena to an attractor which can represent an externum of a function, an image, a solution to a system of ODE, or a stochastic process.
Quantum analog computing quantum mechanics attract↗
Leveraging Hydropower Multi-Sensor Data for Inference and Age-Informed Modeling
Increased demand of operational flexibility such as faster ramp up/down in generation, and more frequent start/stops are putting hydropower plants and their associated components in unprecedented stress. Consequently, these plants are at the high risk of extended and more frequent outage to accommodate unscheduled, and unexpected maintenance. Therefore, hydropower plants are in critical need of data driven and age-informed analysis for their regular and unscheduled operation. Yet not all hydropower plants are exhaustively equipped with sensors and/or measurement streams for their respective components – demanding solutions on how to detect, identify, and locate the cause of any event from the unobservable. Idaho National Laboratory (INL) analyzed the anonymized measurements and event records from the Hydropower Research Institute (HRI) to address this issue, as part of the Water Power Technologies Office (WPTO) funded one year multi-lab project. First, we investigated how time series of multiple sensor measurements can be leveraged to identify an event “root cause” as well as to develop an inference (i.e., estimate the unobservable) problem. INL also investigated how individual hydropower components’ reaction or response times vary across the pre-event, during event, and post-event conditions – enabling the hydropower dynamic models to be age-informed. Finally, the impact of clustering multi-sensor time series on short-term vibration prediction is analyzed. INL will present key findings from these analyses and recommend next steps for stakeholder adoption.
13 HYDRO ENERGY↗