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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Cross-Domain Reasoning for Neuromorphic Model Design

Designing performant neuromorphic models requires reasoning across neuroscience, neuromorphic computing, and machine learning, making it a natural target for cross-domain hypothesis generation. Our primary contribution is a multi-corpus knowledge graph spanning all three domains, which we show substantially increases cross-domain retrieval novelty over single-corpus baselines. We additionally introduce NeuKReAct, an agentic reasoning framework that iteratively retrieves from this graph and synthesizes design hypotheses via a step-by-step blackboard architecture, enabling structured compartmentalization of design decisions. Lastly, we introduce an execution head that translates hypotheses into structured design documents and runnable code. We evaluate novelty using a combinatorial creativity metric that measures cross-domain retrieval distance across the citation graph. Our results confirm that corpus breadth is the dominant driver of novelty. Moreover, we highlight a concrete instance of the novelty-utility tradeoff within NeuKReAct, underscoring a need for joint creativity evaluation, balancing both novelty and utility.

Ramavarapu, Vikram [ORNL] (ORCID:0009000188757213)↗
NASA NTRS2002

The Relationship Between Fidelity and Learning in Aviation Training and Assessment

Flight simulators can be designed to train pilots or assess their flight performance. Low-Fidelity simulators maximize the initial learning rate of novice pilots and minimize initial costs; whereas, expensive, high-fidelity simulators predict the realworld in-flight performance of expert pilots (Fink & Shriver, 1978 Hays & Singer 1989; Kinkade & Wheaton. 1972). Although intuitively appealing and intellectually convenient to generalize concepts of learning and assessment, what holds true for the role of fidelity in assessment may not always hold true for learning, and vice versa. To bring clarity to this issue, the author distinguishes the role of fidelity in learning from its role in assessment as a function of skill level by applying the hypothesis of Alessi (1988) and reviewing the Laughery, Ditzian, and Houtman (1982) study on simulator validity. Alessi hypothesized that there is it point beyond which one additional unit of flight-simulator fidelity results in a diminished rate of learning. The author of this current paper also suggests the existence of an optimal point beyond which one additional unit of flight-simulator fidelity results in a diminished rate of practical assessment of nonexpert pilot performance.

Noble, Cliff↗
DOE OSTI2023

Walking the ‘design–build–test–learn’ cycle: flux analysis and genetic engineering reveal the pliability of plant central metabolism

Oilseeds are of great economic importance for food and animal feed and their contribution to renewable energy production. Soybean seeds (Glycine max (L.) Merr.) contain c. 40% protein, 20% oil, and 30% carbohydrate (Song et al., 2023). Due to the massive scale of soybean production worldwide, even small improvements in seed protein and oil content make economic sense (Song et al., 2023). Successful manipulation of seed composition largely depends on a thorough understanding of the processes and pathways involved in the biosynthesis of fatty acids and amino acids, which are the building blocks of lipids and proteins. Rational engineering of the synthesis of storage reserves, that is, the rerouting of metabolic flux in central metabolism, is difficult to accomplish due to the complexity of the central metabolic network, the intricate regulation of its enzymes at multiple levels, and the often-unpredictable effects of genetic manipulation (Sweetlove et al., 2017). Therefore, the advancement of our understanding of central metabolism and its control of carbon partitioning requires following an iterative ‘design–build–test–learn’ (DBTL) cycle (Lin & Eudes, 2020) where metabolic flux analysis and hypothesis testing by transgenic approaches are important components. Previous metabolic studies on soybeans using isotopic tracers and metabolic flux analysis have provided insight into how lipid and protein biosynthesis occurs simultaneously during seed development (Allen et al., 2009; Allen & Young, 2013; Kambhampati et al., 2021). In an article published in this issue of New Phytologist, Morley et al. (2023; 1834–1851) put the insights they have gained into the delivery of metabolic precursors and energy cofactors to oil synthesis to the test and arrive at a successful metabolic engineering design. They show that an increase in seed oil content in soybeans can be achieved by overexpression of malic enzyme (ME) during seed development. Malic enzyme refers to a class of decarboxylating malate dehydrogenase enzymes that oxidize malate with NAD + or NADP + as redox cofactor while generating pyruvate and CO 2 . Like higher plants in general, soybean has distinct NADH- or NADPH-producing ME isoforms localized to the cytosol, plastid, or mitochondria (Gerrard Wheeler et al., 2016). As Morley et al. show, an increase in seed oil can be achieved in particular when a NADP+-dependent enzyme isoform (EC 1.1.1.40) is overexpressed in the plastid. Given the complex compartmentalization of pyruvate, malate, and redox metabolism (Fig. 1), increased oil production appears to depend on additional pyruvate and reducing equivalents being produced in the same compartment where de novo fatty acid biosynthesis occurs: the plastid.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Using Deep Learning Techniques to Search for the MiniBooNE Low Energy Excess in MicroBooNE with > 3$\sigma$ Sensitivity

This thesis describes an analysis developed for the MicroBooNE experiment to investigate an anomalous excess of electron-like events observed in the MiniBooNE detector. The hypothesis investigated here is that the MiniBooNE anomaly represents appearance of electron neutrinos. Using an amalgam of novel Deep Learning and standard algorithmic techniques this analysis reconstructs and identifies a highly pure sample of charged current quasi-elastic muon neutrino and electron neutrino interactions. This thesis describes the steps in the analysis chain and provides data-to-simulation comparisons for each step that establish confidence in the final prediction. When interpreted in the context of a $\nu e$ appearance like model, this analysis predicts a 3.2$\sigma$ sensitivity to exclude a standard model fluctuation which would appear as a MiniBooNE like anomaly using $7\times10^{20}$ protons on target of MicroBooNE Data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1999

Plasma membrane ordering agent pluronic F-68 (PF-68) reduces neurotransmitter uptake and release and produces learning and memory deficits in rats

A substantial body of evidence indicates that aged-related changes in the fluidity and lipid composition of the plasma membrane contribute to cellular dysfunction in humans and other mammalian species. In the CNS, reductions in neuronal plasma membrane order (PMO) (i.e., increased plasma membrane fluidity) have been attributed to age as well as the presence of the beta-amyloid peptide-25-35, known to play an important role in the neuropathology of Alzheimer's disease (AD). These PMO increases may influence neurotransmitter synthesis, receptor binding, and second messenger systems as well as signal transduction pathways. The effects of neuronal PMO on learning and memory processes have not been adequately investigated, however. Based on the hypothesis that an increase in PMO may alter a number of aspects of synaptic transmission, we investigated several neurochemical and behavioral effects of the membrane ordering agent, PF-68. In cell culture, PF-68 (nmoles/mg SDS extractable protein) reduced [3H]norepinephrine (NE) uptake into differentiated PC-12 cells as well as reduced nicotine stimulated [3H]NE release. The compound (800-2400 microg/kg, i.p., resulting in nmoles/mg SDS extractable protein in the brain) decreased step-through latencies and increased the frequencies of crossing into the unsafe side of the chamber in inhibitory avoidance training. In the Morris water maze, PF-68 increased the latencies and swim distances required to locate a hidden platform and reduced the time spent and distance swam in the previous target quadrant during transfer (probe) trials. PF-68 did not impair performance of a well-learned working memory task, the rat delayed stimulus discrimination task (DSDT), however. Studies with 14C-labeled PF-68 indicated that significant (pmoles/mg wet tissue) levels of the compound entered the brain from peripheral (i.p.) injection. No PF-68 related changes were observed in swim speeds or in visual acuity tests in water maze experiments, rotorod performance, or in tests of general locomotor activity. Furthermore, latencies to select a lever in the DSDT were not affected. These results suggest that PF-68 induced deficits in learning and memory without confounding peripheral motor, sensory, or motivational effects at the tested doses. Furthermore, none of the doses induced a conditioned taste aversion to a novel 0.1% saccharin solution indicating a lack of nausea or gastrointestinal malaise induced by the compound. The data indicate that increases in neuronal plasma membrane order may have significant effects on neurotransmitter function as well as learning and memory processes. Furthermore, compounds such as PF-68 may also offer novel tools for studying the role of neuronal PMO in mnemonic processes and changes in PMO resulting from age-related disorders such as AD.

Poloxamer/pharmacology↗
DOE OSTI2024

Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions

In this study, we develop a machine-learning (ML)-enabled strategy for selecting hillslope-scale ecohydrological monitoring sites within snow-dominated mountainous watersheds, with a particular focus on snow-soil–plant interactions. Data layers rely on spatial data layers from both remote sensing and hydrological model simulations. Specifically, a Landsat-based foresummer drought sensitivity index is used to define the dependency of the annual peak plant productivity on the Palmer drought severity index in the early growing season. Hydrological simulations provide the spatiotemporal dynamics of near-surface soil moisture and snow depth. In this framework, a regression analysis identifies the key hydrological variables relevant to the spatial heterogeneity of drought sensitivity. We then apply unsupervised clustering to these key variables, using the Gaussian mixture model, to group hillslopes into several zones that have divergent relationships regarding soil moisture, snow dynamics, and drought sensitivity. Using the datasets collected in the East River Watershed (Crested Butte, Colorado, United States), results show that drought sensitivity is significantly correlated with model-derived soil moisture and snow-free timing over space and time. The relationship is, however, non-linear, such that the correlation decreases above a threshold elevation and in a heavy snow year due to large snowpacks, lateral flow, and soil storage limitations. Clustering is then able to define the zones that have high or low sensitivity to drought, as well as the mid-elevation regions where sensitivity is associated with the topographic aspect and net potential radiation. In addition, the algorithm identifies the most representative hillslopes with road/trail access within each zone for installing monitoring sites. Our method also aims to significantly increase the use of ML and model-simulation results to guide critical zone and watershed monitoring activities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach

False alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. However, research using deep learning to reduce false alerts often requires the physical and cyber sensor data to be trustworthy. Implicit trust is a major problem for artificial intelligence or machine learning (AI/ML) in cyber-physical system (CPS) security, because when these solutions are most urgently needed is also when they are most at risk (e.g., during an attack). To address this, the Inter-Domain Evidence theoretic Approach for Inference (IDEA-I) is proposed that reframes the detection problem as how to make good decisions given uncertainty. Specifically, an evidence theoretic approach leveraging Dempster–Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from supervised-learning classifiers. Using this model, a location-cum-domain-based fusion framework is proposed to evaluate the detector’s performance using disjunctive, conjunctive, and cautious conjunctive rules. The approach is demonstrated in a cyber-physical power system testbed, and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, and general Bayesian theorem-based metrics as decision functions. To improve the performance, a multi-objective-based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures.

42 ENGINEERING↗
NASA NTRS1991

From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

Ross, Muriel D.↗
DOE OSTI2022

On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning

We demonstrate the first live, autonomous control over neutron diffraction experiments by developing and deploying ANDiE: the autonomous neutron diffraction explorer. Neutron scattering is a unique and versatile characterization technique for probing the magnetic structure and behavior of materials. However, instruments at neutron scattering facilities in the world is limited, and instruments at such facilities are perennially oversubscribed. We demonstrate a significant reduction in experimental time required for neutron diffraction experiments by implementation of autonomous navigation of measurement parameter space through machine learning. Prior scientific knowledge and Bayesian active learning are used to dynamically steer the sequence of measurements. We show that ANDiE can experimentally determine the magnetic ordering transition of both MnO and Fe 1.09 Te all while providing a fivefold enhancement in measurement efficiency. Furthermore, in a hypothesis testing post-processing step, ANDiE can determine transition behavior from a set of possible physical models. ANDiE's active learning approach is broadly applicable to a variety of neutron-based experiments and can open the door for neutron scattering as a tool of accelerated materials discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Comparative modeling reveals the molecular determinants of aneuploidy fitness cost in a wild yeast model

Although implicated as deleterious in many organisms, aneuploidy can underlie rapid phenotypic evolution. However, aneuploidy will be maintained only if the benefit outweighs the cost, which remains incompletely understood. To quantify this cost and the molecular determinants behind it, we generated a panel of chromosome duplications in Saccharomyces cerevisiae and applied comparative modeling and molecular validation to understand aneuploidy toxicity. We show that 74%–94% of the variance in aneuploid strains’ growth rates is explained by the cumulative cost of genes on each chromosome, measured for single-gene duplications using a genomic library, along with the deleterious contribution of small nucleolar RNAs (snoRNAs) and beneficial effects of tRNAs. Machine learning to identify properties of detrimental gene duplicates provided no support for the balance hypothesis of aneuploidy toxicity and instead identified gene length as the best predictor of toxicity. Our results present a generalized framework for the cost of aneuploidy with implications for disease biology and evolution.

genic load↗
DOE OSTI2021

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Ye, Zhijing↗
DOE OSTI2024

Learning likelihood ratios with neural network classifiers

The likelihood ratio is a crucial quantity for statistical inference in science that enables hypothesis testing, construction of confidence intervals, reweighting of distributions, and more. Many modern scientific applications, however, make use of data- or simulation-driven models for which computing the likelihood ratio can be very difficult or even impossible. By applying the so-called “likelihood ratio trick,” approximations of the likelihood ratio may be computed using clever parametrizations of neural network-based classifiers. A number of different neural network setups can be defined to satisfy this procedure, each with varying performance in approximating the likelihood ratio when using finite training data. We present a series of empirical studies detailing the performance of several common loss functionals and parametrizations of the classifier output in approximating the likelihood ratio of two univariate and multivariate Gaussian distributions as well as simulated high-energy particle physics datasets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Altered Cardiac Energetics and Mitochondrial Dysfunction in Hypertrophic Cardiomyopathy

Background: Hypertrophic cardiomyopathy (HCM) is a complex disease partly explained by the effects of individual gene variants on sarcomeric protein biomechanics. At the cellular level, HCM mutations most commonly enhance force production, leading to higher energy demands. Despite significant advances in elucidating sarcomeric structure–function relationships, there is still much to be learned about the mechanisms that link altered cardiac energetics to HCM phenotypes. Here, we test the hypothesis that changes in cardiac energetics represent a common pathophysiologic pathway in HCM. Methods: We performed a comprehensive multiomics profile of the molecular (transcripts, metabolites, and complex lipids), ultrastructural, and functional components of HCM energetics using myocardial samples from 27 HCM patients and 13 normal controls (donor hearts). Results: Integrated omics analysis revealed alterations in a wide array of biochemical pathways with major dysregulation in fatty acid metabolism, reduction of acylcarnitines, and accumulation of free fatty acids. HCM hearts showed evidence of global energetic decompensation manifested by a decrease in high energy phosphate metabolites (ATP, ADP, and phosphocreatine) and a reduction in mitochondrial genes involved in creatine kinase and ATP synthesis. Accompanying these metabolic derangements, electron microscopy showed an increased fraction of severely damaged mitochondria with reduced cristae density, coinciding with reduced citrate synthase activity and mitochondrial oxidative respiration. These mitochondrial abnormalities were associated with elevated reactive oxygen species and reduced antioxidant defenses. However, despite significant mitochondrial injury, HCM hearts failed to upregulate mitophagic clearance. Conclusions: Overall, our findings suggest that perturbed metabolic signaling and mitochondrial dysfunction are common pathogenic mechanisms in patients with HCM. These results highlight potential new drug targets for attenuation of the clinical disease through improving metabolic function and reducing mitochondrial injury.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

NASA GeneLab Multi-study Visualization Portal

NASA GeneLab has helped advance the field of Space Biology by providing a public repository where researchers can store, share, analyze and visualize the results of space flight related omics experiments. The GeneLab data visualization portal allows any user, regardless of bioinformatics knowledge or access to computational resources, to interact with the experimental data, draw their own conclusions, and gain insights about the effects of space on living systems. These tools help democratize scientific research and foster the NASA Open Science initiative. The new multi-study feature of the GeneLab visualization platform allows users to mine study metadata from RNA sequencing (RNA-seq) experiments to identify samples of interest by filtering datasets based on organism, tissue, assay technology type, and/or factor. Once samples are selected from multiple datasets, users can combine and normalize the sample data, then utilize the visualization displays, including Principal Component Analysis (PCA) plots, to assess sample distributions. Finally, users can perform differential gene expression analysis on the combined data and visualize the results through PCA plots, Volcano plots, Pair plots, Heatmap, Ideogram and Gene Set Enrichment Analysis. All user-generated results and visualizations will be available for download. Here, we present a biological study using samples from multiple GeneLab RNA-seq datasets and analyzed using the multi-study visualization platform to demonstrate inter- and intra-study variability, as well as commonly differentially expressed genes between spaceflight and ground control conditions across datasets. This new feature opens a wide range of possibilities and opportunities for further development including combining other assay technology types and integration with batch effect correction techniques and machine learning applications. Overall, this tool allows users to increase the statistical power of individual experiments, validate hypothesis, identify patterns, and opens the door to new and exciting research.

space biology↗
DOE OSTI2026

Particle Filter Based Inference Testing

The primary intent of PAR-FIT (Particle Filter based Inference Testing) is to provide hard inductive evidence that a machine learning model is capable and proven for an individual test input. By examining training data used to form the underlying model functional correlation, an estimate of the reliability that a model will make the correct prediction can be made. The Sequential Probability Ratio Test is used to derive a qualitative evaluation for reliability based on hypothesis testing. The PAR-FIT framework achieves this by implementing a particle filter and the sequential probability ratio test algorithms on the machine learning model training data to determine relevancy of new individual test samples to the training dataset. The kernel function evaluates the local proximity and density of training data used to derive a prediction outcome. Particles are used to probabilistically determine which training data to evaluate for proximity. For test samples that are within a close proximity to and surrounded by multiple training data points, the evaluated reliability of the prediction is high. For test samples that are anomalies not represented by the training dataset, in low density data clusters, or are far from existing data points, the evaluated reliability is low as insufficient training evidence exists to suggest the model is capable of making the correct prediction. Sequential Probability Ratio Test is further used to determine when a hypothesis on whether a signal can be rejected or accepted for use. The ratio test collects sequence information from the particle filter to test whether the signal is anomalous or normal via hypothesis testing of the underlying distributions.

Chen, Edward [Idaho National Laboratory (INL), Ida↗
NASA NTRS1991

Evidence of chaotic pattern in solar flux through a reproducible sequence of period-doubling-type bifurcations

Presented here is a preliminary study of the limits to solar flux intensity prediction, and of whether the general lack of predictability in the solar flux arises from the nonlinear chaotic nature of the Sun's physical activity. Statistical analysis of a chaotic signal can extract only its most gross features, and detailed physical models fail, since even the simplest equations of motion for a nonlinear system can exhibit chaotic behavior. A recent theory by Feigenbaum suggests that nonlinear systems that can be led into chaotic behavior through a sequence of period-doubling bifurcations will exhibit a universal behavior. As the control parameter is increased, the bifurcation points occur in such a way that a proper ratio of these will approach the universal Feigenbaum number. Experimental evidence supporting the applicability of the Feigenbaum scenario to solar flux data is sparse. However, given the hypothesis that the Sun's convection zones are similar to a Rayleigh-Bernard mechanism, we can learn a great deal from the remarkable agreement observed between the prediction by theory (period doubling - a universal route to chaos) and the amplitude decrease of the signal's regular subharmonics. The authors show that period-doubling-type bifurcation is a possible route to a chaotic pattern of solar flux that is distinguishable from the logarithm of its power spectral density. This conclusion is the first positive step toward a reformulation of solar flux by a nonlinear chaotic approach. The ultimate goal of this research is to be able to predict an estimate of the upper and lower bounds for solar flux within its predictable zones. Naturally, it is an important task to identify the time horizons beyond which predictability becomes incompatible with computability.

Ashrafi, S.↗
NASA NTRS1991

Evidence of chaotic pattern in solar flux through a reproducible sequence of period-doubling-type bifurcations

A preliminary study of the limits to solar flux intensity prediction, and of whether the general lack of predictability in the solar flux arises from the nonlinear chaotic nature of the Sun's physical activity is presented. Statistical analysis of a chaotic signal can extract only its most gross features, and detailed physical models fail, since even the simplest equations of motion for a nonlinear system can exhibit chaotic behavior. A recent theory by Feigenbaum suggests that nonlinear systems that can be led into chaotic behavior through a sequence of period-doubling bifurcations will exhibit a universal behavior. As the control parameter is increased, the bifurcation points occur in such a way that a proper ratio of these will approach the universal Feigenbaum number. Experimental evidence supporting the applicability of the Feigenbaum scenario to solar flux data is sparse. However, given the hypothesis that the Sun's convection zones are similar to a Rayleigh-Bernard mechanism, we can learn a great deal from the remarkable agreement observed between the prediction by theory (period doubling - a universal route to chaos) and the amplitude decrease of the signal's regular subharmonics. It is shown that period-doubling-type bifurcation is a possible route to a chaotic pattern of solar flux that is distinguishable from the logarithm of its power spectral density. This conclusion is the first positive step toward a reformulation of solar flux by a nonlinear chaotic approach. The ultimate goal of this research is to be able to predict an estimate of the upper and lower bounds for solar flux within its predictable zones. Naturally, it is an important task to identify the time horizons beyond which predictability becomes incompatible with computability.

Ashrafi, S.↗