Temporal Parameter Research Articles

Beamforming with modified steering vectors for jet noise source location

Prof. Krish Ahuja has a longstanding interest in jet noise source location. His work in this area is grounded in the idea that if the assumed source location is correct, then the sound should obey the inverse square law relative to that point and the phase should be constant along lines originating at that point. He applied this with, conceptually, one microphone in 1985 and two microphones in 1998. In 2006 he commissioned a beamforming system, Array 48, from OptiNav, Inc. His student, Nick Breen, used this to measure subsonic jet noise source location in detail. The NASA-Glenn Research Center also purchased an Array 48. In the current work, a jet noise data set measured by Gary Podboy using Glenn’s array in 2008 is revisited with a new beamforming algorithm, Robust Functional Beamforming, to further support Tam’s two-source model and Breen’s source location. Beamforming with modified steering vectors is performed to measure the parameters of the wavepacket source model from the far field. This process suggested replacing the wavepacket spatial length parameter with a temporal lifetime parameter. Another steering vector modification aimed to measure modes with odd spinning order. It seems to have found them at an apparent location 10 jet diameters removed from the jet, laterally. This is tentatively interpreted as a Mach radius phenomenon like one observed by Csaba Horvath at NASA-Glenn, also using Array 48, to study counter-rotating propeller noise. In an observation unrelated to beamforming, the excess noise measured at 40° from the jet axis as compared with the 90° angle, is fully contained in the first few cross spectral matrix eigenvalues, or Spectral Proper Orthogonal Decomposition modes, in some cases.

Breast cancer diagnosis and prognosis using a high b-value non-Gaussian continuous-time random-walk model

To compare the diagnostic performance of mono-exponential model-derived apparent diffusion coefficient (ADC), continuous-time random-walk (CTRW) model-derived Dm, α, β and their combinations in discriminating malignancy of breast lesions, and investigate the association between model-derived parameters and prognosis-related immunohistochemical indices. A total of 85 patients with breast lesions (51 malignant, 34 benign) were analysed in this retrospective study. Clinical characteristics include oestrogen receptor (ER), progesterone receptor (PR), human epidermal receptor 2 (HER2), and Ki-67. The ADC was fitted using a mono-exponential model (b-values=0, 800 s/mm2), while Dm, α, and β were fitted using a CTRW model. Independent Student's t-test and the Mann-Whitney U-test were used for the comparison of parameters. Discrimination performance was accomplished by receiver operating characteristic (ROC) analysis, and Spearman's correlation analysis was used to explore the association between immunohistochemical indices and diffusion parameters, the statistical significance level was p<0.05. Dm and ADC demonstrated similar performance in differentiating malignant and benign lesions (AUC=0.928 versus 0.930), while the combination of Dm, α, and β could improve the AUC to 0.969. The combined parameter generated by ADC, Dm, α, and β was effective in identifying the ER+/ER- and PR+/PR- patients. Temporal heterogeneity parameter α correlated significantly with the expression of PR. Diffusion parameters derived from the CTRW model could effectively discriminate the malignancy of breast lesions. Meanwhile, the hormone receptor expression could be distinguished by combined diffusion parameters, and have the potential to reflect the prognosis.

Efficient Rate Control in Versatile Video Coding With Adaptive Spatial–Temporal Bit Allocation and Parameter Updating

Despite the fact that Versatile Video Coding (VVC) has achieved superior coding performance, two major problems remain for the rate control (RC) model in VVC. First, the regions concerned by human eyes are not clear enough in the coded video due to the deviation between the target bit allocation strategy of the coding tree unit (CTU) in RC and the human visual attention mechanism (HVAM). Second, there are significant quality fluctuations in the coded video frames due to the inappropriate updating speed. To address the above problems, we propose an efficient rate control (ERC) model. Specifically, in order to make the coded video more consistent with the attention of human eyes, we extract texture and motion-based spatial-temporal information to guide the bit allocation at the CTU level. Furthermore, based on the quasi-Newton algorithm and bit error, we propose an adaptive parameter updating (APU) method with the proper updating speed to precisely control the bits per frame. The proposed ERC outperforms the default RC model of VVC Test Model (VTM) 9.1 by saving the average Bjøntegaard Delta Rate (BD-Rate) on full-frame video sequences by 3.60% and 4.94% under low delay P (LDP) and random access (RA) configurations respectively, with higher bitrate accuracy. Moreover, the Peak Signal-to-Noise Ratio (PSNR) and actual coded bits per frame in the video coded by the proposed ERC are more stable.

Trade‐Offs Between Spatial and Temporal Accuracy of Complementary Relationship Models for Evaporation in an Ungauged Basin

AbstractThe complementary relationship (CR) between actual and potential evaporation has undergone rapid development over the past decades. Commonly, the evaluation of CR models does not comprehensively cover spatial and temporal aspects of model performance; thus, the spatial and temporal accuracy and parameter sensitivity of different CR models remain relatively unknown, especially in ungauged basins. We examine the spatial and temporal performance and parameter sensitivity of four CR models with fixed parameters at the monthly scale over the source region of the Yellow River in China. Additionally, two CR models with distributed parameters were selected as comparisons. Because of the lack of evaporation spatiotemporal “true” values, the corrected water‐balance‐derived evaporation was used as the temporal reference, and the ensemble mean of multiple evaporation products was used as the spatial reference. We find that trade‐offs between the spatial and temporal accuracy of CR models with fixed parameters for basin evaporation should be considered in the application. Although four CR models with fixed parameters exhibited different spatial and temporal performances, their overall performance was generally similar. The parameters of CR models had consistent spatial and temporal sensitivities, and the sensitivity of parameter αe was stronger than that of other parameters. This study demonstrates that CR models with distributed parameters have a promising future in estimating basin or regional evaporation. An integrated spatial and temporal evaluation framework can better distinguish the performance of CR models. The methodologies can be used as a guideline for evaluating and calibrating CR models that match an application's needs.

Dynamic flood frequency analysis for west flowing rivers of Kerala, India

Conventional Flood Frequency Analysis (FFA) may underestimate flood quantiles and increase hydraulic infrastructure vulnerability in changing climates. This study uses annual maximum streamflow data from 17 hydrologic stations along west-flowing rivers in Kerala, India, for Non-Stationary (NS) FFA. The Generalized Extreme Value model with a linear temporal location parameter worked effectively for five stations. Kidangoor and Pattazhy stations must account for non-stationarity for longer return periods (RPs) (>50 years), whereas Neeleeswaram and Perumannu stations must for shorter RPs (<50 years). An extensive study was conducted for Neeleswaram station (Periyar basin) by simulating NS models incorporating four large-scale climate oscillations as covariates. The stationary assumption underestimated flood return levels of 2-year RP by about 61% which increases the flood risk leading to failure of hydraulic infrastructures. It was observed that the best fitted climate-based NS model achieves stationary return level of 150-years RP at 25-years RP itself. The study proved that the climate-based NS models captured the 2018 August Floods in Periyar basin better than stationary and time-based models. The regional variability in FF curve behaviour concludes that NSFFA for Kerala cannot be generalised and must be done at a local-scale.

Stationary and oscillatory patterns of a food chain model with diffusion and predator‐taxis

In this paper, we investigate pattern dynamics in a reaction‐diffusion‐chemotaxis food chain model with predator‐taxis, which extends previous studies of reaction‐diffusion food chain model. By virtue of diffusion semigroup theory, we first prove global classical solvability and boundedness for the considered model over a bounded domain with smooth boundary for arbitrary predator‐taxis sensitivity coefficient. Then the linear stability analysis for the considered model shows that chemotaxis can induce the losing of stability of the unique positive spatially homogeneous steady state via Turing bifurcation and Turing‐spatiotemporal Hopf bifurcation. These bifurcations results in the formation of two kinds of important spatiotemporal patterns: stationary Turing pattern and oscillatory pattern. Simultaneously, the threshold values for Turing bifurcation and Turing‐spatiotemporal Hopf bifurcation are given explicitly. Finally, numerical simulations are performed to illustrate and support our theoretical findings, and some interesting non‐Turing patterns are found in temporal Hopf parameter space by numerical simulation.

Online score statistics for detecting clustered change in network point processes

We consider online monitoring of the network event data to detect local changes in a cluster when the affected data stream distribution shifts from one point process to another with different parameters. Specifically, we are interested in detecting a change point that causes a shift of the underlying data distribution that follows a multivariate Hawkes process with exponential decay temporal kernel, whereby the Hawkes process is considered to account for spatiotemporal correlation between observations. The proposed detection procedure is based on scan score statistics. We derive the asymptotic distribution of the statistic, which enables the self-normalizing property and facilitates the approximation of the instantaneous false alarm probability and the average run length. When detecting a change in the Hawkes process with nonvanishing self-excitation, the procedure does not require estimating the postchange network parameter while assuming the temporal decay parameter, which enjoys computational efficiency. We further present an efficient procedure to accurately determine the false discovery rate via importance sampling, as validated by numerical examples. Using simulated and real stock exchange data, we show the effectiveness of the proposed method in detecting change while enjoying computational efficiency.

Genuinely tenseless: encoding time in Cantonese

Languages without overt marking of tense have been commonly analyzed as having covert tense, either in the form of a phonologically null tense morpheme or a post-LF semantic rule. We argue that the notion of (neo-Reichenbachian) tense is not only unnecessary for the analysis of Cantonese, but also falls short of accounting for temporal reference in this language. Following Pancheva &amp; Zubizarreta (2020, 2021) on Paraguayan Guarani, we propose an analysis of Cantonese that manipulates the temporal parameter of the evaluation context in lieu of tense. A more general contribution of this line of work is the proposal that tense is not a semantic universal.

Estimation of Gait Parameters From Trunk Movement Measured by Doppler Radar

This study proposes a novel method to estimate biomechanical gait parameters using the monostatic continuous wave Doppler radar data corresponding to trunk movement; the use of trunk movement leads to stable gait parameter estimations when compared to the conventional method which is based on leg movements because the received powers of the trunk echoes are larger and more stable than those of the legs. The proposed gait parameter estimation method employs trunk accelerations extracted from radar spectrograms. The accuracies of the gait parameters, such as the step length, swing time, and stance time, estimated using the proposed methods were evaluated by comparing them with the reference motion capture data. We experimentally demonstrated that the proposed trunk-based method estimated gait parameters with accuracy similar to that of the conventional leg-based method. Additionally, the proposed trunk-based method was able to estimate the swing and stance times, whereas the leg-based method failed to estimate them owing to instability of the ankle echoes. Furthermore, the combined method of the trunk and toe data achieved better accuracy in the estimation of swing and stance times. This study is the first to use data on trunk movements (not leg movements) for temporal gait parameter estimation using radar and demonstrated its practicality for realistic situations in clinical and daily gait assessment to grasp the health status of individuals such as risks of future falls and cognitive impairments.

Drivers of uncertainty in precipitation frequency under current and future climate – application to Maryland, USA

The intensity duration frequency (IDF) curves that inform engineering design must reflect the effects of changing climate on extreme precipitation events. These changes can be addressed, in part, by statistically assessing synthetic data/outputs from high-resolution climate model projections to develop IDF curves. The estimation of IDF curves is associated with multiple sources of uncertainty. Most studies that characterize uncertainty in IDF curves under climate change only address the uncertainty due to the choice and processing of climate model outputs, but neglect uncertainty from statistical modeling choices. This study assesses the uncertainty introduced by developing IDF curves for Maryland, USA from model simulations of current and future climate, using statistical methods that are used in U.S. practice. The study analyzes output time series from the North American Regional Climate Change Assessment Program (NARCCAP) suite, which consists of 6 Atmosphere-Ocean General Circulation Models, each spatially downscaled with two different Regional Climate Models. In a separate study, the 3-hour NARCCAP model output was temporally downscaled to 15 min using two different machine learning models. Uncertainty is assessed both across and within models. Across-model uncertainty arises from the differences among synthetic time-series for precipitation and other meteorological variables produced by the 12 NARCCAP climate model projections. Within-model uncertainty arises from the modeling choices used to develop statistical IDF curves from a single climate model time series. These choices include: temporal downscaling method, time-series type, distribution, and parameter estimation method. The choice of climate model (across-model uncertainty) is the dominant contributor under both current and future climate conditions. On the within-model level, the other sources of uncertainty contribute differently for different climate models. The development and application of future-climate IDF curves should acknowledge the uncertainty introduced by statistical modeling choices, as well as by variation among climate model projections.

Statistical Learning of Nonlinear Stochastic Differential Equations from Nonstationary Time Series using Variational Clustering

Parameter estimation for non-stationary stochastic differential equations (SDE) with an arbitrary non-linear drift and non-linear diffusion is accomplished in combination with a non-parametric clustering methodology. Such a model-based clustering approach includes a quadratic programming (QP) problem with equality and inequality constraints. We couple the QP problem to a closed-form likelihood function approach based on suitable Hermite-expansions to approximate the parameter values of the SDE model. The classification problem provides a smooth indicator function, which enables us to recover the underlying temporal parameter modulation of the one-dimensional SDE. As shown by the numerical examples, the clustering approach recovers a hidden functional relationship between the SDE model parameters and an additional auxiliary process. The study builds upon this functional relationship to develop closed-form, non-stationary, data-driven stochastic models for multiscale dynamical systems in real-world applications.

Task Scheduling for Spark Applications With Data Affinity on Heterogeneous Clusters

The Internet of Things (IoT)-enabled applications use sensors and actuators to collect big data, which are processed by big data models, e.g., Spark. Generally, data processing tasks are precedence constrained and the computation results are transmitted to other IoT devices. In this article, we consider the Spark workflow problem of scheduling tasks with data affinity to heterogeneous servers to minimize the maximum completion time. In a Spark instance, jobs are precedence constrained and stages for each job are also precedence constrained. There are a large number of topological stage orders. A balance between task execution times, determined by heterogeneous servers, and transmission times caused by data affinity is difficult to achieve. A scheduling optimization algorithm framework is proposed, which consists of five components: 1) temporal parameter calculation; 2) ready stage adding; 3) task sequencing; 4) resource allocation; and 5) schedule improvement. Strategies for each component are developed. The algorithmic components are statistically calibrated over a comprehensive set of instances. The proposed algorithm is compared to two modified classic algorithms for similar problems on typical scientific workflow instances. The experimental results demonstrate the effectiveness of the proposal for the considered problem.

Ignoring visual distractors: Habituation to onsets is driven by time-based expectation.

An increasing bulk of evidence shows that through different mechanisms, experienced-based or voluntary, reactive or proactive, human beings can attenuate the distracting impact of salient visual, albeit irrelevant, stimuli. Current mechanisms assume that this is achieved by suppressing the salient distractor's features or location at the priority map level, or at lower dimension-based maps levels. However, this functional architecture has so far ignored the role of time in distractors filtering, a key question that we have addressed in the present study. We found that during a visual discrimination task, a "standard" onset distractor, always appearing at the same interval from the beginning of the trial, was subject to habituation. Crucially, however, when the onset distractor was unfrequently presented with an unexpected 1-second delay, it reboosted capture at full strength, while the "standard" distractor continued to remain overall habituated. As predicted by Sokolov's (1963, Annual Review of Physiology, 25[1], 545-580) theory, our results show that habituation mechanisms filter the irrelevant distracting sensory input also on the basis of its temporal parameters. We conclude that habituation to onsets is controlled also by time-based expectation mechanisms and suggest that more recently proposed theories of distractors filtering should also incorporate the temporal parameter among the factors that allow an efficient handling of visual distraction.

A Modal Interpretation of Quantum Spins and Its Application to Freudian Theory

In the present paper, we aim to develop a formal quantum logic theory of the interplay between conscious and unconscious processes of the human mind, a goal that has already been envisaged in quantum cognition; in doing so, we will show how the interplay between formal language and metalanguage allows for characterizing pure quantum states as infinite singletons: in the case of the spin observable, we obtain an equation defining a modality that is then re-interpreted as an abstract projection operator. By including a temporal parameter in the equations and by defining a modal negative operator, we derive an intuitionistic-like negation, for which the non-contradiction law is seen as an equivalent of the quantum uncertainty. Building on the psychoanalytic theory of Bi-Logic by Matte Blanco, we use modalities in interpreting the emergence of conscious representations from an unconscious one, and we demonstrate that this description fits well with Freud’s view of the role of negation in mental processes. Psychoanalysis, where affect plays a prominent role in shaping not only conscious, but also unconscious representations, is therefore seen as a suitable model to expand the domain of quantum cognition to the broader field of affective quantum cognition.

IMU-based assessment of motor control in a population of young subjects with idiopathic scoliosis: preliminary results from the motor-child study

Motor development is an adaptive process occurring within a self-organizing system and influenced by several factors. While evidence exists for motor control in severe pathologies (cerebral palsy), minor musculoskeletal conditions, such as scoliosis, have never been explored. Clinical-functional tests used for the analysis of motor control (Movement ABC-2 or TGMD-2) are time-consuming, hindering their applicability to large populations. In recent years wearable Inertial Measurement Units (IMUs) were introduced for low-cost ambulatory quantitative assessment allowing the estimation of temporal gait parameters [1] and of dynamics of motor control through nonlinear metrics [2–4]. The aim of this work was to assess motor control alterations in children affected by idiopathic scoliosis using clinical- functional tests and IMU-based metrics related to motor control. 34 children aged between 6 and 16 years with scoliosis (ATR ≥ 5 ° or Cobb ≥ 10°) were included. They underwent MovementABC-2 for clinical assessment of motor competence. Three tri-axial IMUs (MetamotionR, mbient-Lab, USA) were attached on the lower back and on the shanks [5] of each child. 3D acceleration and angular velocity data were recorded at 200 Hz while the participants walked at self- selected speed back and forth, two times in normal (NW) and once in tandem walking (TW) along a 15 m path. Stride (StrideT, seconds), stance (StanceT, % of StrideT), and double support time (DS, % of StrideT) were calculated from foot contact events. Temporal parameter variability was calculated as standard deviation. MSE (τ from 1 to 6), related to complexity and automaticity, and RQA, related to pattern regularity, were calculated on trunk acceleration data along the 3 directions. Data were compared to those of matched healthy peers. Movement ABC-2 test: 12 patients received scores above the 50th percentile (35.3%) and 22 below (64.7%). No significant correlation was observed with the degree of scoliosis (Cobb angle) or the degrees of torsion (ATR). Temporal parameters: subjects resulted slower during NW (longer StrideT), with larger dispersion of StanceT and DS than healthy peers. During TW, subjects above 12 years of age resulted slower, with longer StanceT and longer DS for increasing age as compared to their healthy peers. Non-linear metrics: subjects showed larger MSE during NW for lower values of τ (1 to 3) in the ML and V direction and reduced RQA in AP direction for increasing age as compared to healthy peers; during TW, reduced MSE for lower values of τ (1 to 3) in the V and AP direction and increased RQA in AP direction. Children affected by idiopathic scoliosis showed alterations of motor control. In particular, temporal parameters highlighted a slower and less stable gait, more evident for increasing age as related to TW. Lower stability is also related to a less mature motor control with a delay in the development of the automaticity characterizing healthy peers, and a reduced development of the motor complexity required for more demanding locomotor tasks. These preliminary data suggest that idiopathic scoliosis affects motor control in the developing population. IMU-derived metrics demonstrated their effectiveness in complementing clinical assessment.

The future is 2D: spectral-temporal fitting of dynamic MRS data provides exponential gains in precision over conventional approaches.

Many MRS paradigms produce 2D spectral-temporal datasets, including diffusion-weighted, functional, and hyperpolarized and enriched (carbon-13, deuterium) experiments. Conventionally, temporal parameters-such as T2 , T1 , or diffusion constants-are assessed by first fitting each spectrum independently and subsequently fitting a temporal model (1D fitting). We investigated whether simultaneously fitting the entire dataset using a single spectral-temporal model (2D fitting) would improve the precision of the relevant temporal parameter. We derived a Cramer Rao lower bound for the temporal parameters for both 1D and 2D approaches for 2 experiments: a multi-echo experiment designed to estimate metabolite T2 s, and a functional MRS experiment designed to estimate fractional change ( ) in metabolite concentrations. We investigated the dependence of the relative standard deviation (SD) of T2 in multi-echoand in functional MRS. When peaks were spectrally distant, 2D fitting improved precision by approximately 20% relative to 1D fitting, regardless of the experiment and other parameter values. These gains increased exponentially as peaks drew closer. Dependence on temporal model parameters was weak to negligible. Our results strongly support a 2D approach to MRS fitting where applicable, and particularly in nuclei such as hydrogen and deuterium, which exhibit substantial spectral overlap.

Development of 1.3 GHz Medium-β (β = 0.634) Disk-Loaded Deflecting Cavity for 150 keV Electron Beam

A miniaturized 150 kV DC photocathode gun is developed at Peking University to generate electron beam which can be manipulated in temporal and spatial distribution as requirements freely. To measure the bunch length which is an important temporal parameter of the low energy electron beam from the DC photocathode gun, a 1.3 GHz medium-β disk-loaded deflecting cavity is adopted. In this paper we present the design of the deflecting cavity which involves the microwave design including the geometry optimization and the separation of the orthogonal dipole modes as well as the power coupling, the mechanical design including the determination of the cavity wall thickness and the tuning as well as brazing structure, and the thermodynamic analysis. Particle tracking simulation shows that the best resolution of 190 fs can be achieved for the 150 keV electron beam by using the deflecting cavity. Its fabrication is completed and the RF measurements are carried out with a vector network analyzer. It is shown the measured values of the RF physical parameters are in good agreement with the simulation design ones.

Effects of flow loop compressible volume position on system instabilities during flow boiling in micro-channel heat sinks

Much of published literature addressing flow instabilities in thermal management systems employing micro-channel modules has focused on the instability characteristics of the module alone, and far fewer studies have aimed at understanding the relationship between these characteristics and the compressible volume in the flow loop external to the module. From a practical point of view, developers of micro-channel thermal management systems for many modern applications are in pursuit of practical remedies that would greatly mitigate instabilities and their impact on cooling performance. The present study experimentally examines the effects of compressible volume location in a closed pump-driven flow loop designed to deliver FC-72 to a micro-channel test module having 38 channels with 315-µm hydraulic diameter. Three accumulator locations are investigated: upstream of the test module, downstream of the test module, and between the condenser and the pump. Both high-frequency temporal parameter data and high-speed video records are analyzed for ranges of mass velocity and heat flux, with inlet subcooling held constant at ∼14.5 °C. Pressure Drop Oscillation (PDO) is shown to dominate when the accumulator is situated upstream, whereas Parallel Channel Instability (PCI) is dominant for the other two locations. PDO shows severe pressure oscillations across the micro-channel heat sink, with rapid bubble growth and confinement, elongated bubble expansion in both directions, flow stagnation, and flow reversal (including vapor backflow to the inlet plenum) constituting the principal sequence of events characterizing the instability. Spectral analysis of pressure signals is performed using Fast Fourier Transform, which shows PDO extending the inlet pressure fluctuations with the same dominant frequency to other upstream flow loop components, with higher amplitudes closer to the pump exit. From a practical system operation point of view, throttling the flow upstream of the heat sink effectively eliminates PDO but renders PCI dominant, and placing the accumulator in the liquid flow segment of the loop between the condenser and the pump ensures the most stable operation.

Advancements in Gd-based neutron detection (2): Proton–gamma correlation approach

The paper introduces new developments in the conception, optimization, and principle validation of a neutron counter, based on the insertion of gadolinium in plastic scintillators, that is under work at CEA LIST since 2017. The radiation sensor, comprising two volumes of scintillating polymers, it is, by nature, sensitive to both neutron and gamma radiations. This allows the design of a versatile n/γ detector, provided that it is possible to algorithmically separate the signature of both types of radiations. After amplitude and gamma–gamma coincidence filtering, the authors investigate a third n/γ discrimination mode by proton–gamma temporal correlation filtering. After a bibliographic study, the article details the estimation of the temporal parameter useful for such a discrimination, firstly by explicit calculations, secondly by means of a MCNP6.1-based, dedicated simulator. A time-based-processing firmware, embedded into a pulse acquisition card, was then created to compare these estimates with experimental distributions. A 50-μs temporal gate was identified as relevant to discriminate temporal correlations in presence of a neutron emitter. A compensation technique was eventually implemented in order to obtain first experimental estimates of neutron sensitivity, and neutron detections limits in a mixed field. The study ends up providing a maximum reachable sensitivity in the order of 0.3 c.n−1.cm 2 under a fast neutron flux (AmBe), with two 1-L plastic scintillators separated by a 250-μm Gd converter, and the associated temporal correlation and compensation algorithm, as well as neutron detection limits in the same order as those of a He-3 Bonner sphere over acquisition times between 1 and 10 min.

Variational learning of quantum ground states on spiking neuromorphic hardware

SummaryRecent research has demonstrated the usefulness of neural networks as variational ansatz functions for quantum many-body states. However, high-dimensional sampling spaces and transient autocorrelations confront these approaches with a challenging computational bottleneck. Compared to conventional neural networks, physical model devices offer a fast, efficient and inherently parallel substrate capable of related forms of Markov chain Monte Carlo sampling. Here, we demonstrate the ability of a neuromorphic chip to represent the ground states of quantum spin models by variational energy minimization. We develop a training algorithm and apply it to the transverse field Ising model, showing good performance at moderate system sizes (). A systematic hyperparameter study shows that performance depends on sample quality, which is limited by temporal parameter variations on the analog neuromorphic chip. Our work thus provides an important step towards harnessing the capabilities of neuromorphic hardware for tackling the curse of dimensionality in quantum many-body problems.