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ML potentials enable fast TPS simulations ( e.g. , for alanine dipeptide) but may fail for complex cases like azobenzene isomerization. Adequate reference data and domain expertise are crucial for selecting test trajectories.
Here we demonstrate a method to compute the dielectric spectra of fluids in molecular dynamics (MD) by directly applying electric fields to the simulation. We obtain spectra from MD simulations with low magnitude electric fields (≈0.01 V/Å) in agreement with spectra from the fluctuation–dissipation method for water and acetonitrile. We examine this method’s trade-off between noise at low field magnitudes and the nonlinearity of the response at higher field magnitudes. We then apply the Booth equation to describe the nonlinear response of both fluids at low frequency (0.1 GHz) and high field magnitude (up to 0.5 V/Å). We develop a model of the frequency-dependent nonlinear response by combining the Booth description of the static nonlinear dielectric response of fluids with the frequency-dependent linear dielectric response of the Debye model. We find good agreement between our model and the MD simulations of the nonlinear dielectric response for both acetonitrile and water.
The Singular Value Decomposition (SVD) is one of the most important matrix factorizations, enjoying a wide variety of applications across numerous application domains. In statistics and data analysis, the common applications of SVD inclue Principal Components Analysis (PCA) and regression. Usually these applications arise on data that has far more rows than columns, so-called "tall/skinny" matrices. In the big data analytics context, this may take the form of hundreds of millions to billions of rows with only a few hundred columns. There is a need, therefore, for fast, accurate, and scalable tall/skinny SVD implementations which can fully utilize modern computing resources. To that end, we present a survey of three different algorithms for computing the SVD for these kinds of tall/skinny data layouts using MPI for communication. We contextualize these with common big data analytics techniques. Finally, we present both CPU and GPU timing results from the Summit supercomputer, and discuss possible alternative approaches.
Agent-based simulation provides a powerful tool for in silico system modeling. However, these simulations do not provide built-in methods for uncertainty quantification (UQ). Within these types of models a typical approach to UQ is to run multiple realizations of the model then compute aggregate statistics. This approach is limited due to the compute time required for a solution. When faced with an emerging biothreat, public health decisions need to be made quickly and solutions for integrating near real-time data with analytic tools are needed. We propose an integrated Bayesian UQ framework for agent-based models based on sequential Monte Carlo sampling. Given streaming or static data about the evolution of an emerging pathogen this Bayesian framework provides a distribution over the parameters governing the spread of a disease through a population. These estimates of the spread of a disease may be provided to public health agencies seeking to abate the spread. By coupling agent-based simulations with Bayesian modeling in a data assimilation, our proposed framework provides a powerful tool for modeling dynamical systems in silico. We propose a method which reduces model error and provides a range of realistic possible outcomes. Moreover, our method addresses two primary limitations of ABMs: the lack of UQ and an inability to assimilate data. Our proposed framework combines the flexibility of an agent-based model with UQ provided by the Bayesian paradigm in a workflow which scales well to HPC systems. We provide algorithmic details and results on a simulated outbreak with both static and streaming data.
Traditional multimodel methods for estimating future changes in precipitation intensity, duration, and frequency (IDF) curves rely on mean or median of models’ IDF estimates. Such multimodel estimates are impaired by large estimation uncertainty, shadowing their efficacy in planning efforts. Here, assuming that each climate model is one representation of the underlying data generating process, i.e., the Earth system, we propose a novel extension of current methods through pooling model data: (i) evaluate performance of climate models in simulating the spatial and temporal variability of the observed annual maximum precipitation (AMP), (ii) bias-correct and pool historical and future AMP data of reasonably performing models, and (iii) compute IDF estimates in a nonstationary framework from pooled historical and future model data. Pooling enhances fitting of the extreme value distribution to the data and assumes that data from reasonably performing models represent samples from the “true” underlying data generating distribution. Through Monte Carlo simulations with synthetic data, we show that return periods derived from pooled data have smaller biases and lesser uncertainty than those derived from ensembles of individual model data. We apply this method to NA-CORDEX models to estimate changes in 24-h precipitation intensity–frequency (PIF) estimates over the Susquehanna watershed and Florida peninsula. Our approach identifies significant future changes at more stations compared to median-based PIF estimates. The analysis suggests that almost all stations over the Susquehanna and at least two-thirds of the stations over the Florida peninsula will observe significant increases in 24-h precipitation for 2–100-yr return periods.
A theoretical study was conducted to develop an analytical prediction method for helicopter main rotor noise due to the ingestion of atmospheric turbulence. This study incorporates an atmospheric turbulence model, a rotor mean flow contraction model and a rapid distortion turbulence model which together determine the statistics of the non-isotropic turbulence at the rotor plane. Inputs to the combined mean inflow and turbulence models are controlled by atmospheric wind characteristics and helicopter operating conditions. A generalized acoustic source model was used to predict the far field noise generated by the non-isotropic flow incident on the rotor. Absolute levels for acoustic spectra and directivity patterns were calculated for full scale helicopters, without the use of empirical or adjustable constants. Comparisons between isotropic and non-isotropic turbulence at the rotor face demonstrated pronounced differences in acoustic spectra. Turning and contraction of the flow for hover and low speed vertical ascent cases result in a 3 dB increase in the acoustic spectrum energy and a 10 dB increase in tone levels. Compared to trailing edge noise, turbulence ingestion noise is the dominant noise mechanism below approximately 30 rotor harmonics, while above 100 harmonics, trailing edge noise levels exceed turbulence ingestion noise by 25 dB.
A structural power flow approach for the analysis of structure-borne transmission of vibrations is used to analyze the influence of structural parameters on transmitted power. The parametric analysis is also performed using the Statistical Energy Analysis approach and the results are compared with those obtained using the power flow approach. The advantages of structural power flow analysis are demonstrated by comparing the type of results that are obtained by the two analytical methods. Also, to demonstrate that the power flow results represent a direct physical parameter that can be measured on a typical structure, an experimental study of structural power flow is presented. This experimental study presents results for an L shaped beam for which an available solution was already obtained. Various methods to measure vibrational power flow are compared to study their advantages and disadvantages.
The free energy that is dissipated in a magnetic reconnection process of a solar flare, generally accompanied by a coronal mass ejection (CME), has been considered as the ultimate energy source of the global energy budget of solar flares in previous statistical studies. Here we explore the effects of the aerodynamic drag force on CMEs, which supplies additional energy from the slow solar wind to a CME event, besides the magnetic energy supply. For this purpose, we fit the analytical aerodynamic drag model of Cargill and Vršnak et al. to the height–time profiles r(t) of LASCO/SOHO data in 14,316 CME events observed during the first 8 yr (2010–2017) of the Solar Dynamics Observatory era (ensuring EUV coverage with AIA). Our main findings are (1) a mean solar wind speed of w = 472 ± 414 km s(exp −1), (2) a maximum drag-accelerated CME energy of E(drag) <~2 × 10(exp32) erg, (3) a maximum flare-accelerated CME energy of E(flare <~1.5 × 10(exp33) erg, (4) the ratio of the summed kinetic energies of all flare accelerated CMEs to the drag-accelerated CMEs amounts to a factor of 4, (5) the inclusion of the drag force slightly lowers the overall energy budget of CME kinetic energies in flares from ≈7% to ≈4%, and (6) the arrival times of CMEs at Earth can be predicted with an accuracy of ≈23%.
Pulsed nuclear magnetic resonance (NMR) is widely used in high-precision magnetic field measurements. The absolute value of the magnetic field is determined from the precession frequency of nuclear magnetic moments. The Hilbert transform is widely used to extract the phase function from the observed free induction decay (FID) signal and then its frequency. In this paper, a detailed implementation of a Hilbert-transform based FID frequency extraction method is described. How artifacts and noise level in the FID signal affect the extracted phase function are derived analytically. A method of mitigating the artifacts in the extracted phase function of an FID is discussed. Correlations between noises of the phase function samples are studied for different noise spectra. We discovered that the error covariance matrix for the extracted phase function is nearly singular and improper for constructing the $\chi^2$ used in the fitting routine. A down-sampling method for fixing the singular covariance matrix has been developed, so that the minimum $\chi^2$-fit yields properly the statistical uncertainty of the extracted frequency. Other practical methods of obtaining the statistical uncertainty are also discussed.
This work provides a broad overview of the separation processes used to isolate actinides and the experimentally and computationally determined chemical characteristics that define those separations. The redox chemistry of the actinides plays a pivotal role in both aqueous and pyrochemical processing separations. The near-overlapping energies of the 6d and 5f orbitals in the light actinides allow for facile adjustment of actinide oxidation states, which is used in many established separation methods. In contrast, the stable, generally 3+ oxidation states of the mid- and heavy actinides can make it difficult to separate them from the similarly lanthanides(III). In aqueous separations, the tendency of the actinides to form anionic and neutral aqueous complexes with a variety of complexants (especially soft donors) is used to achieve high separation factors between chemically similar elements in both solid–liquid separations and liquid–liquid extraction. This selectivity can be further tuned through the use of specialized organic or solid-phase ligands. Pyroprocessing separations utilize the unique redox behavior of the actinides to adjust their distribution between a molten salt electrolyte and either a solid electrode or molten metal phase. Atomic-level insights into the mechanisms underlying actinide separation processes, with the ultimate goal of predicting separation behavior, can be provided by electronic structure and statistical mechanical-based calculation methods.
The magnitude of the vacuum expectation value of the Gukov-Vafa-Witten superpotential |W o | plays a central role in the phenomenology of type IIB flux compactifications. Recent analytical constructions have shown that perturbatively flat vacua can be used to obtain very low values of |W o |. We present systematic algorithms to carry out exhaustive numerical searches for such vacua. Here we also analyse them in the statistical context, as part of the entire ensemble of type IIB flux vacua at low |W o |. Our preliminary analysis indicates that these perturbatively flat vacua are statistically sparse in the whole set of vacua at low |W o | as calculated by Denef and Douglas. Two-moduli examples are used to illustrate these more general findings in specific settings. We find that these simple (two moduli) cases are good examples for existence proofs but they do not feature a large statistical tuning freedom for phenomenological applications.
This report presents a comprehensive summary of five advanced anomaly detection tools developed and deployed by Oak Ridge National Laboratory in support of the VA’s Health Information Technology modernization. These detectors—Order Path Tracker, Trend Watcher, Pain Pointer, Performance Monitor, and Patient Record Flag Detector—leverage statistical and machine learning methods to monitor workflow disruptions, detect anomalies in care sequences and volumes, identify bottlenecks, and track system-level performance metrics across VistA and Millennium systems. All detectors have been integrated into the Health Data Analytics Platform (HDAP), with most having completed deployment and testing using live data from targeted stations in cardiology and oncology domains. This work enhances VA’s capacity for proactive system surveillance, promotes patient safety, and informs data-driven operational improvements across the EHR ecosystem.
We consider two streams of data or measurements with disparate qualities and time resolutions that need to be classified. The first stream consists of higher quality data at a coarser time resolution, and the other consists of lower quality data at a finer time resolution. We present a fuser-switch method that fuses the set of classifiers of each stream separately and switches between them. We show that this method provides classification decisions at a finer time resolution with superior detection and false alarm probabilities compared to individual classifiers, under the statistical independence and time resolution ratio conditions. When classifiers are trained using machine learning methods, we show that this superior performance is guaranteed with a confidence probability specified by the classifiers' generalization equations. We use these results to provide analytical foundations for previous practical results that achieved significant performance improvements in classifying Pu/Np target dissolution events at a radiochemical processing facility.
An assessment of the long-term collision risk is made for an uncontrolled orbiting spacecraft that repeatedly enters the spatial region occupied by the satellites. Numerical data are obtained for 50-year orbit propagations which account approximately for the major perturbative effects of Jupiter oblateness, solar gravity and satellite gravity. A broad statistical viewpoint regarding the question of collision likelihood is adopted and is based on a total sample size of 480 initial orbits distributed among 4 orbit classes, 8 inclinations and 15 initial epochs. Numerical results compare favorably with an analytical prediction formula which shows that eccentric, low-inclination orbits have high collision probability.
An assessment of the long-term collision risk is made for an uncontrolled orbiting spacecraft that repeatedly enters the spatial region occupied by the satellites. Numerical data are obtained for 50-year orbit propagations which account approximately for the major perturbative effects of Jupiter oblateness, solar gravity and satellite gravity. A broad statistical viewpoint regarding the question of collision likelihood is adopted and is based on a total sample size of 480 initial orbits distributed among 4 orbit classes, 8 inclinations and 15 initial epochs. Numerical results compare favorably with an analytical prediction formula which shows that eccentric low-inclination orbits have high collision probability.
The influence of bearing misalignment upon the dynamic characteristics of statistically indeterminant rotor bearing systems is investigated. Both bearing loads and stability speed limits of a rotor may be changed significantly by magnitude and direction of bearing misalignment. The useful theory of short journal bearings is introduced and simple analytical expressions, governing the misalignment problem, are carried out. Polar plots for the bearing load capacities and stability maps, describing the speed limit in terms of misalignment, are presented. These plots can be used by the designer to estimate deviations between calculation and experimental data due to misalignment effects.
Earlier studies of solar-induced variations in stratospheric parameters have been mainly concerned with observed ozone responses. In the present investigation, attention is given to temperature responses as well as ozone responses at low latitudes, taking into account 22 months of Nimbus 7 solar backscattered ultraviolet (SBUV) ozone and stratospheric and mesospheric sounder temperature data. A data description is provided, and cross-correlation and regression analyses are conducted. An extension is considered of an analytic model, which was derived by Frederick (1981) for the coupled behavior of ozone and temperature perturbations in the upper stratosphere and lower mesosphere. The extended model is applied to the results of the analyses. The obtained data provide statistical evidence for ozone and temperature responses to changes in solar ultraviolet flux on the time scale of the solar rotation period.
A statistical theory of geodynamo action is developed, using a mathematical model of the geodynamo as a rotating outer core containing an ideal (i.e., no dissipation), incompressible, turbulent, convecting magnetofluid. On the concentric inner and outer spherical bounding surfaces the normal components of the velocity, magnetic field, vorticity and electric current are zero, as is the temperature fluctuation. This allows the use of a set of Galerkin expansion functions that are common to both velocity and magnetic field, as well as vorticity, current and the temperature fluctuation. The resulting dynamical system, based on the Boussinesq form of the magnetohydrodynamic (MHD) equations, represents MHD turbulence in a spherical domain. These basic equations (minus the temperature equation) and boundary conditions have been used previously in numerical simulations of forced, decaying MHD turbulence inside a sphere [1,2]. Here, the ideal case is studied through statistical analysis and leads to a prediction that an ideal coherent structure will be found in the form of a large-scale quasistationary magnetic field that results from broken ergodicity, an effect that has been previously studied both analytically and numerically for homogeneous MHD turbulence [3,4]. The axial dipole component becomes prominent when there is a relatively large magnetic helicity (proportional to the global correlation of magnetic vector potential and magnetic field) and a stationary, nonzero cross helicity (proportional to the global correlation of velocity and magnetic field). The expected angle of the dipole moment vector with respect to the rotation axis is found to decrease to a minimum as the average cross helicity increases for a fixed value of magnetic helicity and then to increase again when average cross helicity approaches its maximum possible value. Only a relatively small value of cross helicity is needed to produce a dipole moment vector that is aligned at approx.10deg with the rotation axis.