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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Derivation of coarse-grained potentials via multistate iterative Boltzmann inversion

In this work, an extension is proposed to the standard iterative Boltzmann inversion (IBI) method used to derive coarse-grained potentials. It is shown that the inclusion of target data from multiple states yields a less state-dependent potential, and is thus better suited to simulate systems over a range of thermodynamic states than the standard IBI method. The inclusion of target data from multiple states forces the algorithm to sample regions of potential phase space that match the radial distribution function at multiple state points, thus producing a derived potential that is more representative of the underlying interactions. It is shown that the algorithm is able to converge to the true potential for a system where the underlying potential is known. It is also shown that potentials derived via the proposed method better predict the behavior of n-alkane chains than those derived via the standard IBI method. Additionally, through the examination of alkane monolayers, it is shown that the relative weight given to each state in the fitting procedure can impact bulk system properties, allowing the potentials to be further tuned in order to match the properties of reference atomistic and/or experimental systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Geometric integrator for simulations in the canonical ensemble

We introduce a geometric integrator for molecular dynamics simulations of physical systems in the canonical ensemble that preserves the invariant distribution in equations arising from the density dynamics algorithm, with any possible type of thermostat. Our integrator thus constitutes a unified framework that allows the study and comparison of different thermostats and of their influence on the equilibrium and non-equilibrium (thermo-)dynamic properties of a system. To show the validity and the generality of the integrator, we implement it with a second-order, time-reversible method and apply it to the simulation of a Lennard-Jones system with three different thermostats, obtaining good conservation of the geometrical properties and recovering the expected thermodynamic results. Moreover, to show the advantage of our geometric integrator over a non-geometric one, we compare the results with those obtained by using the non-geometric Gear integrator, which is frequently used to perform simulations in the canonical ensemble. The non-geometric integrator induces a drift in the invariant quantity, while our integrator has no such drift, thus ensuring that the system is effectively sampling the correct ensemble.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Minimax rational approximation of the Fermi-Dirac distribution

Accurate rational approximations of the Fermi-Dirac distribution are a useful component in many numerical algorithms for electronic structure calculations. The best known approximations use O(log(βΔ)log(ϵ –1 )) poles to achieve an error tolerance ϵ at temperature β –1 over an energy interval Δ. We apply minimax approximation to reduce the number of poles by a factor of four and replace Δ with Δ occ , the occupied energy interval. Furthermore, this is particularly beneficial when Δ >> Δ occ , such as in electronic structure calculations that use a large basis set.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations

We construct a new framework for accelerating Markov chain Monte Carlo in posterior sampling problems where standard methods are limited by the computational cost of the likelihood, or of numerical models embedded therein. Our approach introduces local approximations of these models into the Metropolis–Hastings kernel, borrowing ideas from deterministic approximation theory, optimization, and experimental design. Previous efforts at integrating approximate models into inference typically sacrifice either the sampler’s exactness or efficiency; our work seeks to address these limitations by exploiting useful convergence characteristics of local approximations. We prove the ergodicity of our approximate Markov chain, showing that it samples asymptotically from the exact posterior distribution of interest. We describe variations of the algorithm that employ either local polynomial approximations or local Gaussian process regressors. Our theoretical results reinforce the key observation underlying this article: when the likelihood has some local regularity, the number of model evaluations per Markov chain Monte Carlo (MCMC) step can be greatly reduced without biasing the Monte Carlo average. Numerical experiments demonstrate multiple order-of-magnitude reductions in the number of forward model evaluations used in representative ordinary differential equation (ODE) and partial differential equation (PDE) inference problems, with both synthetic and real data. Supplementary materials for this article are available online.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A fast particle-mesh simulation of non-linear cosmological structure formation with massive neutrinos

Quasi-N-body simulations, such as FastPM, provide a fast way to simulate cosmological structure formation, but have yet to adequately include the effects of massive neutrinos. In this work, we present a method to include neutrino particles in FastPM, enabling computation of the CDM and total matter power spectra to percent-level accuracy in the non-linear regime. The CDM-neutrino cross-power can also be computed at a sufficient accuracy to constrain cosmological observables. To avoid the shot noise that typically plagues neutrino particle simulations, we employ a quasi-random algorithm to sample the relevant Fermi-Dirac distribution when setting the initial neutrino thermal velocities. We additionally develop an effective distribution function to describe a set of non-degenerate neutrinos as a single particle to speed up non-degenerate simulations. The simulation is accurate for the full range of physical interest, M ν ≲ 0.6eV, and applicable to redshifts z ≲ 2. Such accuracy can be achieved by initializing particles with the two-fluid approximation transfer functions (using the REPS package). Convergence can be reached in ~ 25 steps, with a starting redshift of z=99. Probing progressively smaller scales only requires an increase in the number of CDM particles being simulated, while the number of neutrino particles can remain fixed at a value less than or similar to the number of CDM particles. In turn, the percentage increase in runtime-per-step due to neutrino particles is between ~ 5-20% for runs with 1024 3 CDM particles, and decreases as the number of CDM particles is increased. The code has been made publicly available, providing an invaluable resource to produce fast predictions for cosmological surveys and studying reconstruction.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

‘Flux+Mutability’: a conditional generative approach to one-class classification and anomaly detection

Abstract Anomaly Detection is becoming increasingly popular within the experimental physics community. At experiments such as the Large Hadron Collider, anomaly detection is growing in interest for finding new physics beyond the Standard Model. This paper details the implementation of a novel Machine Learning architecture, called Flux+Mutability, which combines cutting-edge conditional generative models with clustering algorithms. In the ‘flux’ stage we learn the distribution of a reference class. The ‘mutability’ stage at inference addresses if data significantly deviates from the reference class. We demonstrate the validity of our approach and its connection to multiple problems spanning from one-class classification to anomaly detection. In particular, we apply our method to the isolation of neutral showers in an electromagnetic calorimeter and show its performance in detecting anomalous dijets events from standard QCD background. This approach limits assumptions on the reference sample and remains agnostic to the complementary class of objects of a given problem. We describe the possibility of dynamically generating a reference population and defining selection criteria via quantile cuts. Remarkably this flexible architecture can be deployed for a wide range of problems, and applications like multi-class classification or data quality control are left for further exploration.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Non-Gaussian estimates of tensions in cosmological parameters

We discuss how to efficiently and reliably estimate the level of agreement and disagreement on parameter determinations from different experiments, fully taking into account non-Gaussianities in the parameter posteriors. We develop two families of scalable algorithms that allow us to perform this type of calculations in increasing number of dimensions and for different levels of tensions. One family of algorithms rely on kernel density estimates of posterior distributions while the other relies on machine learning modeling of the posterior distribution with normalizing flows. We showcase their effectiveness and accuracy with a set of benchmark examples and find both methods agree with each other and the true tension within 0.5σ in difficult cases and generally to 0.2σ or better. This allows us to study the level of internal agreement between different measurements of the clustering of cosmological structures from the Dark Energy Survey and their agreement with measurements of the Cosmic Microwave Background from the Planck satellite.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Petascale Application of a Coupled CPU-GPU Algorithm for Simulation and Analysis of Multiphase Flow Solutions in Porous Medium Systems, In: 2014 IEEE 28th International Parallel and Distributed Processing Symposium

Large-scale simulation can provide a wide range of information needed to develop and validate theoretical models for multiphase flow in porous medium systems. In this paper, we consider a coupled solution in which a multiphase flow simulator is coupled to an analysis approach used to extract the interfacial geometries as the flow evolves. This has been implemented using MPI to target heterogeneous nodes equipped with GPUs. The GPUs evolve the multiphase flow solution using the lattice Boltzmann method while the CPUs compute up scaled measures of the morphology and topology of the phase distributions and their rate of evolution. Our approach is demonstrated to scale to 4,096 GPUs and 65,536 CPU cores to achieve a maximum performance of 244,754 million-lattice-node updates per second (MLUPS) in double precision execution on Titan. In turn, this approach increases the size of systems that can be considered by an order of magnitude compared with previous work and enables detailed in situ tracking of averaged flow quantities at temporal resolutions that were previously impossible. Furthermore, it virtually eliminates the need for post-processing and intensive I/O and mitigates the potential loss of data associated with node failures.

Computer Science↗
DOE OSTI2023

Feeder Power Disaggregation: A Data-Efficient Matrix Completion Approach

This paper presents a data-driven algorithm for the feeder power disaggregation problem in distribution systems. Leveraging spatio-temporal power patterns in residential homes, residential power is discomposed into three components: sparse-switching loads, periodic loads, and photovoltaic (PV) generation, which are characterized through the design of two sparse matrices and a low-rank matrix. The matrix completion process is data-efficient because of the matrix sparsity and low rankness, along with the use of power system models. The proposed approach is tested using real-world residential data set on a 33-bus distribution system, demonstrating accurate power disaggregation with efficient matrix completion.

distribution system↗
DOE OSTI2013

Dosimetric evaluation of photon dose calculation under jaw and MLC shielding

Purpose: The accuracy of photon dose calculation algorithms in out-of-field regions is often neglected, despite its importance for organs at risk and peripheral dose evaluation. The present work has assessed this for the anisotropic analytical algorithm (AAA) and the Acuros-XB algorithms implemented in the Eclipse treatment planning system. Specifically, the regions shielded by the jaw, or the MLC, or both MLC and jaw for flattened and unflattened beams have been studied.Methods: The accuracy in out-of-field dose under different conditions was studied for two different algorithms. Measured depth doses out of the field, for different field sizes and various distances from the beam edge were compared with the corresponding AAA and Acuros-XB calculations in water. Four volumetric modulated arc therapy plans (in the RapidArc form) were optimized in a water equivalent phantom, PTW Octavius, to obtain a region always shielded by the MLC (or MLC and jaw) during the delivery. Doses to different points located in the shielded region and in a target-like structure were measured with an ion chamber, and results were compared with the AAA and Acuros-XB calculations. Photon beams of 6 and 10 MV, flattened and unflattened were used for the tests.Results: Good agreement between calculated and measured depth doses was found using both algorithms for all points measured at depth greater than 3 cm. The mean dose differences (±1SD) were −8%± 16%, −3%± 15%, −16%± 18%, and −9%± 16% for measurements vs AAA calculations and −10%± 14%, −5%± 12%, −19%± 17%, and −13%± 14% for Acuros-XB, for 6X, 6 flattening-filter free (FFF), 10X, and 10FFF beams, respectively. The same figures for dose differences relative to the open beam central axis dose were: −0.1%± 0.3%, 0.0%± 0.4%, −0.3%± 0.3%, and −0.1%± 0.3% for AAA and −0.2%± 0.4%, −0.1%± 0.4%, −0.5%± 0.5%, and −0.3%± 0.4% for Acuros-XB. Buildup dose was overestimated with AAA, while Acuros-XB gave results more consistent with measurements. From RapidArc plan analysis the average difference between calculation and measurement in the shielded region was −0.3%± 0.4% and −2.5%± 1.2% for AAA and Acuros-XB, respectively, relative to the mean target dose value (1.6%± 2.3%, −12.7%± 4.0% if relative to each local value). These values were compared with the corresponding differences in the target structure: −0.7%± 2.3% for AAA, and −0.5%± 2.3% for Acuros-XB.Conclusions: The two algorithms analyzed showed encouraging results in predicting out-of-field region dose for clinical use.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2014

Feasibility of using intermediate x-ray energies for highly conformal extracranial radiotherapy

Purpose: To investigate the feasibility of using intermediate energy 2 MV x-rays for extracranial robotic intensity modulated radiation therapy. Methods: Two megavolts flattening filter free x-rays were simulated using the Monte Carlo code MCNP (v4c). A convolution/superposition dose calculation program was tuned to match the Monte Carlo calculation. The modeled 2 MV x-rays and actual 6 MV flattened x-rays from existing Varian Linacs were used in integrated beam orientation and fluence optimization for a head and neck, a liver, a lung, and a partial breast treatment. A column generation algorithm was used for the intensity modulation and beam orientation optimization. Identical optimization parameters were applied in three different planning modes for each site: 2, 6 MV, and dual energy 2/6 MV. Results: Excellent agreement was observed between the convolution/superposition and the Monte Carlo calculated percent depth dose profiles. For the patient plans, overall, the 2/6 MV x-ray plans had the best dosimetry followed by 2 MV only and 6 MV only plans. Between the two single energy plans, the PTV coverage was equivalent but 2 MV x-rays improved organs-at-risk sparing. For the head and neck case, the 2MV plan reduced lips, mandible, tongue, oral cavity, brain, larynx, left and right parotid gland mean doses by 14%, 8%, 4%, 14%, 24%, 6%, 30% and 16%, respectively. For the liver case, the 2 MV plan reduced the liver and body mean doses by 17% and 18%, respectively. For the lung case, lung V20, V10, and V5 were reduced by 13%, 25%, and 30%, respectively. V10 of heart with 2 MV plan was reduced by 59%. For the partial breast treatment, the 2 MV plan reduced the mean dose to the ipsilateral and contralateral lungs by 27% and 47%, respectively. The mean body dose was reduced by 16%. Conclusions: The authors showed the feasibility of using flattening filter free 2 MV x-rays for extracranial treatments as evidenced by equivalent or superior dosimetry compared to 6 MV plans using the same inverse noncoplanar intensity modulated planning method.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

A new background distribution-based active contour model for three-dimensional lesion segmentation in breast DCE-MRI

Purpose: To develop and evaluate a computerized semiautomatic segmentation method for accurate extraction of three-dimensional lesions from dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) of the breast. Methods: The authors propose a new background distribution-based active contour model using level set (BDACMLS) to segment lesions in breast DCE-MRIs. The method starts with manual selection of a region of interest (ROI) that contains the entire lesion in a single slice where the lesion is enhanced. Then the lesion volume from the volume data of interest, which is captured automatically, is separated. The core idea of BDACMLS is a new signed pressure function which is based solely on the intensity distribution combined with pathophysiological basis. To compare the algorithm results, two experienced radiologists delineated all lesions jointly to obtain the ground truth. In addition, results generated by other different methods based on level set (LS) are also compared with the authors’ method. Finally, the performance of the proposed method is evaluated by several region-based metrics such as the overlap ratio. Results: Forty-two studies with 46 lesions that contain 29 benign and 17 malignant lesions are evaluated. The dataset includes various typical pathologies of the breast such as invasive ductal carcinoma, ductal carcinomain situ, scar carcinoma, phyllodes tumor, breast cysts, fibroadenoma, etc. The overlap ratio for BDACMLS with respect to manual segmentation is 79.55% ± 12.60% (mean ± s.d.). Conclusions: A new active contour model method has been developed and shown to successfully segment breast DCE-MRI three-dimensional lesions. The results from this model correspond more closely to manual segmentation, solve the weak-edge-passed problem, and improve the robustness in segmenting different lesions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-T-585: Commissioning of Electron Monte Carlo in Eclipse Treatment Planning System for TrueBeam

Purpose: To commission electron Monte Carlo (eMC) algorithm in Eclipse Treatment Planning System (TPS) for TrueBeam Linacs, including the evaluation of dose calculation accuracy for small fields and oblique beams and comparison with the existing eMC model for Clinacs. Methods: Electron beam percent-depth-dose (PDDs) and profiles with and without applicators, as well as output factors, were measured from two Varian TrueBeam machines. Measured data were compared against the Varian TrueBeam Representative Beam Data (VTBRBD). The selected data set was transferred into Eclipse for beam configuration. Dose calculation accuracy from eMC was evaluated for open fields, small cut-out fields, and oblique beams at different incident angles. The TrueBeam data was compared to the existing Clinac data and eMC model to evaluate the differences among Linac types. Results: Our measured data indicated that electron beam PDDs from our TrueBeam machines are well matched to those from our Varian Clinac machines, but in-air profiles, cone factors and open-filed output factors are significantly different. The data from our two TrueBeam machines were well represented by the VTBRBD. Variations of TrueBeam PDDs and profiles were within the 2% /2mm criteria for all energies, and the output factors for fields with and without applicators all agree within 2%. Obliquity factor for two clinically relevant applicator sizes (10×10 and 15×15 cm{sup 2}) and three oblique angles (15, 30, and 45 degree) were measured for nominal R100, R90, and R80 of each electron beam energy. Comparisons of calculations using eMC of obliquity factors and cut-out factors versus measurements will be presented. Conclusion: eMC algorithm in Eclipse TPS can be configured using the VTBRBD. Significant differences between TrueBeam and Clinacs were found in in-air profiles and open field output factors. The accuracy of the eMC algorithm was evaluated for a wide range of cut-out factors and oblique incidence.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Parallel Memory-Independent Communication Bounds for SYRK

In this paper, we focus on the parallel communication cost of multiplying a matrix with its transpose, known as a symmetric rank-k update (SYRK). SYRK requires half the computation of general matrix multiplication because of the symmetry of the output matrix. Recent work (Beaumont et al., SPAA '22) has demonstrated that the sequential I/O complexity of SYRK is also a constant factor smaller than that of general matrix multiplication. Inspired by this progress, we establish memory-independent parallel communication lower bounds for SYRK with smaller constants than general matrix multiplication, and we show that these constants are tight by presenting communication-optimal algorithms. The crux of the lower bound proof relies on extending a key geometric inequality to symmetric computations and analytically solving a constrained nonlinear optimization problem. Here, the optimal algorithms use a triangular blocking scheme for parallel distribution of the symmetric output matrix and corresponding computation.

Communication costs↗
DOE OSTI2017

foresee™ (SWR-17-15)

foresee™ is a home energy management system (HEMS) that provides a user centric energy automation solution for residential building occupants. Built upon advanced control and machine learning algorithms, foresee™ intelligently manages the home appliances and distributed energy resources (DERs) such as photovoltaics and battery storage in a home. Unlike existing HEMS in the market, foresee™ provides a tailored home automation solution for individual occupants by learning and adapting to their preferences on cost, comfort, convenience and carbon. foresee™ improves not only the energy efficiency of the home but also its capability to provide grid services such as demand response. Highly reliable demand response services are likely to be incentivized by utility companies, making foresee™ economically viable for most homes.

Source record↗
DOE OSTI2021

Improving probabilistic infectious disease forecasting through coherence

With an estimated $10.4 billion in medical costs and 31.4 million outpatient visits each year, influenza poses a serious burden of disease in the United States. To provide insights and advance warning into the spread of influenza, the U.S. Centers for Disease Control and Prevention (CDC) runs a challenge for forecasting weighted influenza-like illness (wILI) at the national and regional level. Many models produce independent forecasts for each geographical unit, ignoring the constraint that the national wILI is a weighted sum of regional wILI, where the weights correspond to the population size of the region. We propose a novel algorithm that transforms a set of independent forecast distributions to obey this constraint, which we refer to as probabilistically coherent. Enforcing probabilistic coherence led to an increase in forecast skill for 79% of the models we tested over multiple flu seasons, highlighting the importance of respecting the forecasting system’s geographical hierarchy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Dynamically Scheduled Region-Based Image Compositing

Algorithms for sort-last parallel volume rendering on large distributed memory machines usually divide a dataset equally across all nodes for rendering. Depending on the features that a user wants to see in a dataset, all the nodes will rarely finish rendering at the same time. Here, existing compositing algorithms do not often take this into consideration, which can lead to significant delays when nodes that are compositing wait for other nodes that are still rendering. In this paper, we present an image compositing algorithm that uses spatial and temporal awareness to dynamically schedule the exchange of regions in an image and progressively composite images as they become available. Running on the Edison supercomputer at NERSC, we show that a scheduler-based algorithm with awareness of the spatial contribution from each rendering node can outperform traditional image compositing algorithms.

97 MATHEMATICS AND COMPUTING↗