Search NASA⌕ Search

SEARCH · Search NASA

Results for “Long Short-term memory”

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.

125 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

LLM-Based Adaptive Distribution Voltage Regulation Under Frequent Topology Changes: An In-Context MPC Framework

This paper proposes a large language model (LLM) based adaptive inverter control for distribution voltage regulation under frequent topology changes. We leverage the ability of the LLM to perform in-context learning and create a topology-adaptive surrogate model for power flow calculation. The surrogate model is then integrated with a long short-term memory-based load forecaster and a model predictive control (MPC) scheme to achieve the optimal inverter control that adapts to frequent topology changes. Unlike many existing works that assume fixed-topology grids or require the knowledge of all possible topologies when training a model, the proposed in-context MPC method tackles the distribution voltage control problem under various topologies and adapts to unknown topologies with limited data requirement for fine-tuning. The effectiveness of our method is demonstrated on a modified IEEE 123-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Deep learning-assisted modeling for χ (2) nonlinear optics

Modeling second-order (χ(2)) nonlinear optical processes remains computationally expensive due to the need to resolve fast field oscillations and simulate wave propagation using methods such as the split-step Fourier method (SSFM). This can become a bottleneck in real-time applications, such as high-repetition-rate laser systems requiring rapid feedback and control. We present a long short-term memory-based surrogate model trained on SSFM simulations generated from a start-to-end model of the photocathode drive laser at SLAC National Accelerator Laboratory’s Linac Coherent Light Source II. The model achieves over 250× speedup while maintaining high fidelity, enabling future real-time optimization and laying the foundation for data-integrated modeling frameworks and digital twins of laser systems.

Accelerator Physics (physics.acc-ph)↗
DOE OSTI2025

Physics-Informed Recurrent Neural Networks to Predict Reactor Operations of the AGN-201 Nuclear Reactor

4 page paper submitted to ANS Student conference. Summary of paper similar to the following abstract: The ability to predict how a reactor will operate, understand when anomalous conditions arise, and ensure a reactor is being operated as expected is crucial for deploying new nuclear facilities. Digital twins serve as a unique solution to recognizing reactor behavior; however, they require data to be useful. For next-generation reactors, this data may not currently be available. To explore how synthetic physics-informed reactor data can be used to predict reactor operations, a recurrent neural network was implemented for the Idaho State University AGN-201 digital twin. The goal of this work is to determine how synthetic data can be used to train a recurrent neural network model for predicting the reactor power of the AGN-201. The recurrent neural network was validated using both synthetic and real operational data. We envision this approach will help bridge the gap between the virtual and physical sides of a digital twin, where reactor physics models based on as-built data can be corrected for actual operating parameters to ensure the virtual model mirrors reality.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Interlayer Exciton Polarons in Mesoscopic V 2 O 5 for Broadband Optoelectronic Synapses

Persistent photoconductivity and optoelectronic synaptic behavior are demonstrated in solution-processed mesoscopic α-phase vanadium pentoxide (V 2 O 5 ) thin films. First-principles simulations coupled with the two-site Holstein polaron hopping model show that vacancies at the terminal oxygen position lead to long recombination times because photoexcited electrons and holes reside on different layers separated by the van der Waals gap, forming a weakly coupled interlayer exciton polaron. Mid-gap polaronic states also significantly broaden the photoresponse of the films to span across visible and infrared wavelengths. By controlling the amplitude/intensity, duration, and/or number of optical pulses, tunable optoelectronic memory functions, such as short-term and long-term plasticity, are experimentally established in V 2 O 5 -based optoelectronic synapses. Furthermore, device fabrication was extended to mechanically flexible ultrathin glass substrates. Flexible optoelectronic synapses maintained high performance after 150 bending cycles.

Phan, Thanh Luan [National Renewable Energy Labora↗
NASA NTRS1998

Two memories for geographical slant: separation and interdependence of action and awareness

The present study extended previous findings of geographical slant perception, in which verbal judgments of the incline of hills were greatly overestimated but motoric (haptic) adjustments were much more accurate. In judging slant from memory following a brief or extended time delay, subjects' verbal judgments were greater than those given when viewing hills. Motoric estimates differed depending on the length of the delay and place of response. With a short delay, motoric adjustments made in the proximity of the hill did not differ from those evoked during perception. When given a longer delay or when taken away from the hill, subjects' motoric responses increased along with the increase in verbal reports. These results suggest two different memorial influences on action. With a short delay at the hill, memory for visual guidance is separate from the explicit memory informing the conscious response. With short or long delays away from the hill, short-term visual guidance memory no longer persists, and both motor and verbal responses are driven by an explicit representation. These results support recent research involving visual guidance from memory, where actions become influenced by conscious awareness, and provide evidence for communication between the "what" and "how" visual processing systems.

NASA Discipline Space Human Factors↗
DOE OSTI2026

Protonic nickelate device networks for spatiotemporal neuromorphic computing

Computation in biological neural circuits arises from the interplay of nonlinear temporal responses and spatially distributed dynamic network interactions. Replicating this richness in hardware has remained challenging, as most neuromorphic devices emulate only isolated neuron- or synapse-like functions. Here we introduce an integrated neuromorphic computing platform in which both nonlinear spatiotemporal processing and programmable memory are realized within a single perovskite nickelate material system. By engineering symmetric and asymmetric hydrogenated NdNiO 3 junction devices on the same wafer, we combine ultrafast, proton-mediated transient dynamics with stable multilevel resistance states. Networks of symmetric NdNiO 3 junctions exhibit emergent spatial interactions mediated by proton redistribution, while each node simultaneously provides short-term temporal memory, enabling nanosecond-scale operation with an energy cost of ~0.2 nJ per input. When interfaced with asymmetric output units serving as reconfigurable long-term weights, these networks allow both feature transformation and linear classification in the same material system. Leveraging these emergent interactions, the platform enables real-time pattern recognition and achieves high accuracy in spoken digit classification and early seizure detection, outperforming temporal-only or uncoupled architectures. These results position protonic nickelates as a compact, energy-efficient, CMOS-compatible platform that integrates processing and memory for scalable intelligent hardware.

Electrical and electronic engineering↗
DOE OSTI2024

Selective consolidation of learning and memory via recall-gated plasticity

In a variety of species and behavioral contexts, learning and memory formation recruits two neural systems, with initial plasticity in one system being consolidated into the other over time. Moreover, consolidation is known to be selective; that is, some experiences are more likely to be consolidated into long-term memory than others. Here, we propose and analyze a model that captures common computational principles underlying such phenomena. The key component of this model is a mechanism by which a long-term learning and memory system prioritizes the storage of synaptic changes that are consistent with prior updates to the short-term system. This mechanism, which we refer to as recall-gated consolidation, has the effect of shielding long-term memory from spurious synaptic changes, enabling it to focus on reliable signals in the environment. We describe neural circuit implementations of this model for different types of learning problems, including supervised learning, reinforcement learning, and autoassociative memory storage. These implementations involve synaptic plasticity rules modulated by factors such as prediction accuracy, decision confidence, or familiarity. We then develop an analytical theory of the learning and memory performance of the model, in comparison to alternatives relying only on synapse-local consolidation mechanisms. We find that recall-gated consolidation provides significant advantages, substantially amplifying the signal-to-noise ratio with which memories can be stored in noisy environments. We show that recall-gated consolidation gives rise to a number of phenomena that are present in behavioral learning paradigms, including spaced learning effects, task-dependent rates of consolidation, and differing neural representations in short- and long-term pathways.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Selective consolidation of learning and memory via recall-gated plasticity

In a variety of species and behavioral contexts, learning and memory formation recruits two neural systems, with initial plasticity in one system being consolidated into the other over time. Moreover, consolidation is known to be selective; that is, some experiences are more likely to be consolidated into long-term memory than others. Here, we propose and analyze a model that captures common computational principles underlying such phenomena. The key component of this model is a mechanism by which a long-term learning and memory system prioritizes the storage of synaptic changes that are consistent with prior updates to the short-term system. This mechanism, which we refer to as recall-gated consolidation, has the effect of shielding long-term memory from spurious synaptic changes, enabling it to focus on reliable signals in the environment. We describe neural circuit implementations of this model for different types of learning problems, including supervised learning, reinforcement learning, and autoassociative memory storage. These implementations involve synaptic plasticity rules modulated by factors such as prediction accuracy, decision confidence, or familiarity. We then develop an analytical theory of the learning and memory performance of the model, in comparison to alternatives relying only on synapse-local consolidation mechanisms. We find that recall-gated consolidation provides significant advantages, substantially amplifying the signal-to-noise ratio with which memories can be stored in noisy environments. We show that recall-gated consolidation gives rise to a number of phenomena that are present in behavioral learning paradigms, including spaced learning effects, task-dependent rates of consolidation, and differing neural representations in short- and long-term pathways.

Lindsey, Jack W. (ORCID:0000000309307327)↗
NASA NTRS1999

Timescales of Soil Moisture Anomalies: Results from Two GCMs

Soil moisture anomalies dissipate over timescales that may span weeks to months. Characterizing the geographical and seasonal variations in these timescales can have important practical benefit; significant soil moisture "memory" allows long-lead forecasts of soil moisture, which have been found in recent studies to be essential for useful Ion--lead forecasts of precipitation in many regions. In this talk, we will present and compare the soil moisture timescales derived in two separate general circulation model (GCM) studies. Both studies employ multiple ensembles of short-term climate simulations. Timescales at a given point are effectively estimated by determining how quickly the soil moisture distribution generated in one ensemble of simulations (characterized by a unique set of initial soil moisture conditions) approaches that produced by another ensemble (characterized by a different set of initial soil moisture conditions). The talk will include a discussion of why the timescales produced by the two GCMs differ in some regions, and it will also describe the impact of soil moisture memory on simulated precipitation.

Koster, Randal D.↗
DOE OSTI2025

Short-Term Electric Load Forecasting for a Residential Household in Alaska

Accurate short-term load forecasting at a fine scale is essential for demand response programs, peak shaving, and load-shedding strategies [1]. While traditionally, only aggregate short-term consumption data was available, advanced metering infrastructure (AMI) now provides data at the individual consumer level [1]. There is increasing interest in utilizing this data for short-term load forecasting (from an hour to a few days) to optimize grid operations. Electricity consumption in individual households is highly influenced by residents’ personal behaviors [2]. As a result, unlike aggregate loads, electrical power usage in single households often shows significant volatility, making meter-level load forecasting for individual users particularly challenging [3], [4]. Deep learning methods, with their strong ability to model nonlinear data, have become popular for improving the accuracy of household electricity consumption forecasting [4]. Notably, the Long ShortTerm Memory (LSTM) has attracted significant attention [5], [6].

42 ENGINEERING↗
NASA NTRS1993

A cross-disciplinary response to improve test activities: The corporate memory capitalization in Ariane4 test domain

The Assembly, Integration, Test, and Validation (AIT/AIV) of the Ariane4 Vehicle Equipment Bay was held at Matra Marconi Space (MMS) site of Toulouse for several years. For this activity, incident interpretation necessitates a great deal of different knowledge. When complex faults occur, particularly those appearing during overall control tests, experts of various domains (EGSE, software, on-board equipment) have to join for investigation sessions. Thus, an assistance tool for the identification of faulty equipment will improve the efficiency of diagnosis and the overall productivity of test activities. As a solution, the Aramiihs laboratory proposed considering the opportunity of a knowledge based system intended to assist the tester in diagnosis. This knowledge based system is, in fact, a short-term achievement of a long-term goal which is the capitalization of corporate memory in the Ariane4 test domain. Aramiihs is a research unit where engineers from MMS and researchers from the IRIT-CNRS cooperate on problems concerning new types of man-system interaction.

Vo, Dinh Phuoc↗
DOE OSTI2024

Mixed Delay/Nondelay Embeddings Based Neuromorphic Computing with Patterned Nanomagnet Arrays

Patterned nanomagnet arrays (PNAs) have been shown to exhibit a strong geometrically frustrated dipole interaction. Some PNAs have also shown emergent domain wall dynamics. Previous works have demonstrated methods to physically probe these magnetization dynamics of PNAs to realize neuromorphic reservoir systems that exhibit chaotic dynamical behavior and high-dimensional nonlinearity. These PNA reservoir systems from prior works leverage echo state properties and linear/nonlinear short-term memory of component reservoir nodes to map and preserve the dynamical information of the input time-series data into nondelay spatial embeddings. Such mappings enable these PNA reservoir systems to imitate and predict/forecast the input time series data. However, these prior PNA reservoir systems are based solely on the nondelay spatial embeddings obtained at component reservoir nodes. As a result, they require a massive number of component reservoir nodes, or a very large spatial embedding (i.e., high-dimensional spatial embedding) per reservoir node, or both, to achieve acceptable imitation and prediction accuracy. These requirements reduce the practical feasibility of such PNA reservoir systems. To address this shortcoming, we present a mixed delay/nondelay embeddings-based PNA reservoir system. Our system uses a single PNA reservoir node with the ability to obtain a mixture of delay/nondelay embeddings of the dynamical information of the time-series data applied at the input of a single PNA reservoir node. Our analysis shows that when these mixed delay/nondelay embeddings are used to train a perceptron at the output layer, our reservoir system outperforms existing PNA-based reservoir systems for the imitation of NARMA 2, NARMA 5, NARMA 7, and NARMA 10 time series data, and for the short-term and long-term prediction of the Mackey Glass time series data.

Ti, Changpeng↗
NASA NTRS1979

On the design of a postprocessor for a search for extraterrestrial intelligence /SETI/ system

The design of an on-line postprocessor for a search for extraterrestrial intelligence (SETI) system is described. Signal processing tasks of the postprocessor include: (1) analysis of power level, phase coherence, and state of polarization of single-channel signals in a search for significant signals; (2) grouping or aggregation of adjacent channel data, time averaging of data; and (3) the detection of drifting and modulated signals. Control functions include multichannel spectrum analyzer frequency and clock control, system calibration and selfdiagnostic, control of data flow to and from short-term and long-term (archival) memories, and operation of detection subsystems, such as a visual display and a tunable receiver.

Healy, T. J.↗
NASA NTRS1987

Analytical design of intelligent machines

The problem of designing 'intelligent machines' to operate in uncertain environments with minimum supervision or interaction with a human operator is examined. The structure of an 'intelligent machine' is defined to be the structure of a Hierarchically Intelligent Control System, composed of three levels hierarchically ordered according to the principle of 'increasing precision with decreasing intelligence', namely: the organizational level, performing general information processing tasks in association with a long-term memory; the coordination level, dealing with specific information processing tasks with a short-term memory; and the control level, which performs the execution of various tasks through hardware using feedback control methods. The behavior of such a machine may be managed by controls with special considerations and its 'intelligence' is directly related to the derivation of a compatible measure that associates the intelligence of the higher levels with the concept of entropy, which is a sufficient analytic measure that unifies the treatment of all the levels of an 'intelligent machine' as the mathematical problem of finding the right sequence of internal decisions and controls for a system structured in the order of intelligence and inverse order of precision such that it minimizes its total entropy. A case study on the automatic maintenance of a nuclear plant illustrates the proposed approach.

Saridis, George N.↗
NASA NTRS2003

The Effect of Spaceflight on the Ultrastructure of the Cerebellum

In weightlessness, astronauts and cosmonauts may experience postural illusions as well as motion sickness symptoms known as the space adaptation syndrome. Upon return to Earth, they have irregularities in posture and balance. The adaptation to microgravity and subsequent re-adaptation to Earth occurs over several days. At the cellular level, a process called neuronal plasticity may mediate this adaptation. The term plasticity refers to the flexibility and modifiability in the architecture and functions of the nervous system. In fact, plastic changes are thought to underlie not just behavioral adaptation, but also the more generalized phenomena of learning and memory. The goal of this experiment was to identify some of the structural alterations that occur in the rat brain during the sensory and motor adaptation to microgravity. One brain region where plasticity has been studied extensively is the cerebellar cortex-a structure thought to be critical for motor control, coordination, the timing of movements, and, most relevant to the present experiment, motor learning. Also, there are direct as well as indirect connections between projections from the gravity-sensing otolith organs and several subregions of the cerebellum. We tested the hypothesis that alterations in the ultrastructural (the structure within the cell) architecture of rat cerebellar cortex occur during the early period of adaptation to microgravity, as the cerebellum adapts to the absence of the usual gravitational inputs. The results show ultrastructural evidence for neuronal plasticity in the central nervous system of adult rats after 24 hours of spaceflight. Qualitative studies conducted on tissue from the cerebellar cortex (specifically, the nodulus of the cerebellum) indicate that ultrastructural signs of plasticity are present in the cerebellar zones that receive input from the gravity-sensing organs in the inner ear (the otoliths). These changes are not observed in this region in cagematched ground control animals. The specific changes include the formation of lamellar bodies, profoundly enlarged Purkinje cell mitochondria, the presence of inter-neuronal cellular protrusions in the molecular layer, and signs of degeneration in the distal dendrites of the Purkinje cells. Since these morphologic signs are not apparent in the control animals, they are not likely to be due to caging or tissue processing effects. The particular nature of the structural alterations in the nodulus, most notably the formation of lamellar bodies and the presence of degeneration, further suggests that excitotoxicity (damaging overstimulation of neurons) may play a role in the short-term neural response to spaceflight. These findings suggest a structural basis for the neuronal and synaptic plasticity accompanying the central nervous system response to altered gravity and help identify the cellular bases underlying the vestibular abnormalities experienced by astronauts during periods of adaptation and re-adaptation to different gravitational forces. Also, since the short- and long-term changes in neural structure occurring during such periods of adaptation resemble the neuronal alterations that occur in some neurologic disorders such as stroke, these findings may offer guidance in the development of strategies for rehabilitation and treatment of such disorders.

Holstein, Gay R.↗
NASA NTRS1998

Working and strategic memory deficits in schizophrenia

Working memory and its contribution to performance on strategic memory tests in schizophrenia were studied. Patients (n = 18) and control participants (n = 15), all men, received tests of immediate memory (forward digit span), working memory (listening, computation, and backward digit span), and long-term strategic (free recall, temporal order, and self-ordered pointing) and nonstrategic (recognition) memory. Schizophrenia patients performed worse on all tests. Education, verbal intelligence, and immediate memory capacity did not account for deficits in working memory in schizophrenia patients. Reduced working memory capacity accounted for group differences in strategic memory but not in recognition memory. Working memory impairment may be central to the profile of impaired cognitive performance in schizophrenia and is consistent with hypothesized frontal lobe dysfunction associated with this disease. Additional medial-temporal dysfunction may account for the recognition memory deficit.

Memory Disorders/etiology/physiopathology↗
NASA NTRS2009

Got Mercury?

Many of the operational and payload lighting units used in various spacecraft contain elemental mercury. If these devices were damaged on-orbit, elemental mercury could be released into the cabin. Although there are plans to replace operational units with alternate light sources, such as LEDs, that do not contain mercury, mercury-containing lamps efficiently produce high quality illumination and may never be completely replaced on orbit. Therefore, exposure to elemental mercury during spaceflight will remain possible and represents a toxicological hazard. Elemental mercury is a liquid metal that vaporizes slowly at room temperature. However, it may be completely vaporized at the elevated operating temperatures of lamps. Although liquid mercury is not readily absorbed through the skin or digestive tract, mercury vapors are efficiently absorbed through the respiratory tract. Therefore, the amount of mercury in the vapor form must be estimated. For mercury releases from lamps that are not being operated, we utilized a study conducted by the New Jersey Department of Environmental Quality to calculate the amount of mercury vapor expected to form over a 2-week period. For longer missions and for mercury releases occurring when lamps are operating, we conservatively assumed complete volatilization of the available mercury. Because current spacecraft environmental control systems are unable to remove mercury vapors, both short-term and long-term exposures to mercury vapors are possible. Acute exposure to high concentrations of mercury vapors can cause irritation of the respiratory tract and behavioral symptoms, such as irritability and hyperactivity. Chronic exposure can result in damage to the nervous system (tremors, memory loss, insomnia, etc.) and kidneys (proteinurea). Therefore, the JSC Toxicology Group recommends that stringent safety controls and verifications (vibrational testing, etc.) be applied to any hardware that contains elemental mercury that could yield airborne mercury vapor concentrations greater than 0.1 mg/cu m in the total spacecraft atmosphere for exposures lasting 30 days or less or 0.01 mg/cu m mercury vapor for exposures lasting more than 30 days. We also encourage the use of alternative devices that do not contain mercury.

Meyers, Valerie E.↗