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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

A simple method for finding the scattering coefficients of quantum graphs

Quantum walks are roughly analogous to classical random walks, and similar to classical walks they have been used to find new (quantum) algorithms. When studying the behavior of large graphs or combinations of graphs, it is useful to find the response of a subgraph to signals of different frequencies. In doing so, we can replace an entire subgraph with a single vertex with variable scattering coefficients. In this paper, a simple technique for quickly finding the scattering coefficients of any discrete-time quantum graph will be presented. These scattering coefficients can be expressed entirely in terms of the characteristic polynomial of the graph’s time step operator. This is a marked improvement over previous techniques which have traditionally required finding eigenstates for a given eigenvalue, which is far more computationally costly. With the scattering coefficients we can easily derive the “impulse response” which is the key to predicting the response of a graph to any signal. This gives us a powerful set of tools for rapidly understanding the behavior of graphs or for reducing a large graph into its constituent subgraphs regardless of how they are connected.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Triangle Method for Dense ReLU Layers [SWR-25-72]

This software is an implementation of the methods for initializing and training neural networks to be more efficient per parameter, described more fully below and in the related publication: In theory, depth should make a ReLU network EXPONENTIALLY more efficient by enabling it to produce an exponential number of piecewise linear sections in its output. This reasoning is largely based on the work of mathematicians that have hand-constructed networks that make good use of depth. In practice however, even very deep ReLU networks that have been randomly initialized will behave identically to their shallow counterparts - missing an entire exponential dimension of efficiency. The triangle method is a first attempt at realizing the exponential potential of deep networks. Instead of randomly setting weights, we force pairs of neurons in each layer learn to build triangles (i.e. functions from [0,1] -> [0,1] that look like triangles). This is a very efficient pattern for generating lots of linear pieces because composing two triangular functions doubles the number of pieces with each composition. The triangle method is more than just a different initialization, it is a new paradigm of training. Instead of making direct updates to the matrix weights, we do an extra step of backpropagation to collect the derivatives of the loss function with respect to the shapes of the triangles, training them to tilt left or right. This process essentially holds the networks hand throughout the loss landscape and forces it to always use depth effectively by producing triangular shapes internally. This can produce several orders of magnitude of improvement on convex one-dimensional regression problems. Much more theoretical work is needed to realize its full potential beyond this context, but the implementation in this repository will still work in arbitrary numbers of dimensions. The file Triangle_Method.py is a generalized form of the method that will build each neuron its own custom 1-d convex activation function (with exponential efficiency). Example usage on one dimensional problems can be found in Example_Usage.ipynb and an example of using this in a real neural network can be found in Example_VGG16_CIFAR10.ipynb.

Milkert, Max [National Renewable Energy Laboratory↗
DOE OSTI2015

SU-E-E-05: Improving Contouring Precision and Consistency for Physicians-In-Training with Simple Lab Experiments

Purpose: Target contouring for high-dose treatments such as radiosurgery of brain metastases is highly critical in eliminating marginal failure and reducing complications as shown by recent clinical studies. In order to improve contouring accuracy and practice consistency for the procedure, we introduced a self-assessed physics lab practice for the physicians-in-training. Methods: A set of commercially acquired high-precision PMMA plastic spheres were randomly embedded in a Styrofoam block and then scanned with the CT/MR via the clinical procedural imaging protocol. A group of first-year physicians-in-training (n=6) from either neurosurgery or radiation oncology department were asked to contour the scanned objects (diameter ranged from 0.4 cm to 3.8 cm). These user-defined contours were then compared with the ideal contour sets of object shape for self assessments to determine the maximum areas of the observed discrepancies and method of improvements. Results: The largest discrepancies from initial practice were consistently found to be located near the extreme longitudinal portions of the target for all the residents. Discrepancy was especially prominent when contouring small objects < 1.0 cm in diameters. For example, the mean volumes rendered from the initial contour data set differed from the ideal data set by 7.7%±6.6% for the participants (p> 0.23 suggesting agreement cannot be established). However, when incorporating a secondary imaging scan such as reconstructed coronal or sagittal images in a repeat practice, the agreement was dramatically improved yielding p<0.02 in agreement with the reference data set for all the participants. Conclusion: A simple physics lab revealed a common pitfall in contouring small metastatic brain tumors for radiosurgical procedures and provided a systematic tool for physicians-in-training in improving their clinical contouring skills. Dr Ma is current a board member of international stereotactic radiosurgical society.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble

In this work we explore the capability of physics-informed neural networks (PINNs) to discover multiple solutions. Many real-world phenomena governed by nonlinear differential equations (DEs), such as fluid flow, exhibit multiple solutions under the same conditions, yet capturing this solution multiplicity remains a significant challenge. A key difficulty lies in providing appropriate initial conditions or guesses, as widely used time-marching schemes and Newton’s method are highly sensitive to these choices when solving complex computational problems. While machine learning models, particularly PINNs, have shown promise in solving DEs, their ability to capture multiple solutions remains underexplored. In this work, we propose a simple and practical approach using PINNs to learn and discover multiple solutions. We first demonstrate that PINNs, when combined with random initialization and deep ensemble method—originally developed for uncertainty quantification—can effectively uncover multiple solutions to nonlinear ordinary and partial DEs. Although training large ensembles of PINNs may appear computationally demanding, this can be done efficiently using vectorization techniques supported by modern deep learning frameworks, allowing many networks to be trained simultaneously. Our approach highlights the critical role of initialization in shaping solution diversity, addressing an often-overlooked aspect of machine learning for scientific computing. Furthermore, we propose utilizing PINN-generated solutions as initial conditions or initial guesses for conventional numerical solvers to enhance accuracy and efficiency in capturing multiple solutions. Extensive numerical experiments, including the Allen–Cahn equation and cavity flow, where our approach successfully identifies both stable and unstable solutions, validate the effectiveness of our method. These findings establish a general and efficient framework for addressing solution multiplicity in nonlinear DEs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Measuring representative volume elements from high-resolution grain-scale strain fields

Most crystalline materials present a highly heterogeneous response at the microscale, which can be affected by both internal factors (such as microstructural parameters) and external factors (such as loading). Relating microscale inhomogeneities to the macroscale response of a material requires the use of homogenisation techniques, usually based on the concept of a representative volume element (RVE)—the smallest volume of material that represents the global average response. Here, in this work, we present a new and robust experimental method of measuring the size of a strain-based RVE from high-resolution grain-scale strain fields obtained using digital image correlation (DIC). The proposed method is based on the statistical (stereological) nature of the RVE, which has been widely adopted in numerical studies, and involves dividing a strain field into randomly selected regions of varying sizes and statistically analysing the distributions of average strains within them. To validate the new method, we generate a large number of synthetic strain fields from a fractional Gaussian noise algorithm. The proposed stereological method is shown to be capable of producing reliable RVE measurements from a very large range of possible microscale strain fields while at the same time being robust in that it can produce RVE measurement results even in cases where other existing methods may be unable to do so. The proposed method has a low field-of-view requirement, only needing a field-of-view about 1.2 times as large as the RVE to produce reliable measurements. In addition, the stereological method offers significant flexibility since its statistical nature allows for control over how strict the RVE measurement should be in each case.

36 MATERIALS SCIENCE↗
DOE OSTI2018

On Collisions Times of ‘Self-Sorting’ Interacting Particles in One-Dimension with Random Initial Positions and Velocities

We investigate a one-dimensional system of N particles, initially distributed with random positions and velocities, interacting through binary collisions. The collision rule is such that there is a time after which the N particles do not interact and become sorted according to their velocities. When the collisions are elastic, we derive asymptotic distributions for the final collision time of a single particle and the final collision time of the system as the number of particles approaches infinity, under different assumptions for the initial distributions of the particles’ positions and velocities. For comparison, a numerical investigation is carried out to determine how a non-elastic collision rule, which conserves neither momentum nor energy, affects the median collision time of a particle and the median final collision time of the system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

A weighted ℓ{sub 1}-minimization approach for sparse polynomial chaos expansions

This work proposes a method for sparse polynomial chaos (PC) approximation of high-dimensional stochastic functions based on non-adapted random sampling. We modify the standard ℓ{sub 1}-minimization algorithm, originally proposed in the context of compressive sampling, using a priori information about the decay of the PC coefficients, when available, and refer to the resulting algorithm as weightedℓ{sub 1}-minimization. We provide conditions under which we may guarantee recovery using this weighted scheme. Numerical tests are used to compare the weighted and non-weighted methods for the recovery of solutions to two differential equations with high-dimensional random inputs: a boundary value problem with a random elliptic operator and a 2-D thermally driven cavity flow with random boundary condition.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Localization dynamics in a centrally coupled system

In systems in which interactions couple a central degree of freedom and a bath, one would expect signatures of the bath's phase to be reflected in the dynamics of the central degree of freedom. This has been recently explored in connection with many-body localized baths coupled with a central qubit or a single-cavity mode - systems with growing experimental relevance in various platforms. Such models also have an interesting connection with Floquet many-body localization via quantizing the external drive, although this has been relatively unexplored. Here we adapt the multilayer multiconfigurational time-dependent Hartree (ML-MCTDH) method, a well-known tree tensor network algorithm, to numerically simulate the dynamics of a central degree of freedom, represented by a d-level system (qudit), coupled to a disordered interacting one-dimensional spin bath. ML-MCTDH allows us to reach ≈10 2 lattice sites, a far larger system size than what is feasible with exact diagonalization or kernel polynomial methods. From the intermediate time dynamics, we find a well-defined thermodynamic limit for the qudit dynamics upon appropriate rescaling of the system-bath coupling. The spin system shows similar scaling collapse in the Edward-Anderson spin-glass order parameter or entanglement entropy at relatively short times. At longer timescales, we see slow growth of the entanglement, which may arise from dephasing mechanisms in the localized system or long-range interactions mediated by the central degree of freedom. Similar signs of localization are shown to appear as well with unscaled system-bath coupling.

1-dimensional spin chains↗
DOE OSTI2016

Is There a Role for Pelvic Irradiation in Localized Prostate Adenocarcinoma? Update of the Long-Term Survival Results of the GETUG-01 Randomized Study

Purpose: To report the long-term results of the French Genitourinary Study Group (GETUG)-01 study in terms of event-free survival (EFS) and overall survival (OS) and assess the potential interaction between hormonotherapy and pelvic nodes irradiation. Patients and Methods: Between December 1998 and June 2004, 446 patients with T1b-T3, N0pNx, M0 prostate carcinoma were randomly assigned to either pelvic nodes and prostate or prostate-only radiation therapy. Patients were stratified into 2 groups: “low risk” (T1-T2 and Gleason score 6 and prostate-specific antigen <3× the upper normal limit of the laboratory) (92 patients) versus “high risk” (T3 or Gleason score >6 or prostate-specific antigen >3× the upper normal limit of the laboratory). Short-term 6-month neoadjuvant and concomitant hormonal therapy was allowed only for high-risk patients. Radiation therapy was delivered with a 3-dimensional conformal technique, using a 4-field technique for the pelvic volume (46 Gy). The total dose recommended to the prostate moved from 66 Gy to 70 Gy during the course of the study. Criteria for EFS included biologic prostate-specific antigen recurrences and/or a local or metastatic progression. Results: With a median follow-up of 11.4 years, the 10-year OS and EFS were similar in the 2 treatment arms. A higher but nonsignificant EFS was observed in the low-risk subgroup in favor of pelvic nodes radiation therapy (77.2% vs 62.5%; P=.18). A post hoc subgroup analysis showed a significant benefit of pelvic irradiation when the risk of lymph node involvement was <15% (Roach formula). This benefit seemed to be limited to patients who did not receive hormonal therapy. Conclusion: Pelvic nodes irradiation did not statistically improve EFS or OS in the whole population but may be beneficial in selected low- and intermediate-risk prostate cancer patients treated with exclusive radiation therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

A global significance evaluation method using simulated events

In High-Energy Physics experiments it is often necessary to evaluate the global statistical significance of apparent resonances observed in invariant mass spectra. One approach to determining significance is to use simulated events to find the probability of a random fluctuation in the background mimicking a real signal. As a high school summer project, we demonstrate a method with Monte Carlo simulated events to evaluate the global significance of a potential resonance with some assumptions. This method for determining significance is general and can be applied, with appropriate modification, to other resonances.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

A Randomized Phase 2 Study of Neoadjuvant Chemoradiaton Therapy With 5-Fluorouracil/Leucovorin or Irinotecan/S-1 in Patients With Locally Advanced Rectal Cancer

Purpose: The purpose of this study was to evaluate the rate of pathologic complete response (pCR) in patients with locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiation therapy (CRT) with leucovorin (FL) versus irinotecan/S-1 (IS). Methods and Materials: Patients with resectable LARC (clinical stage T3/4, lymph node positive, or both) were randomly assigned to receive preoperative radiation (45-50.4 Gy in 25 to 28 daily fractions) and concomitant chemotherapy either with a bolus injection of FL (400 mg/m{sup 2}/day 5-fluorouracil and 20 mg/m{sup 2}/day leucovorin) for 3 consecutive days every 4 weeks for 2 cycles (FL group) or with 40 mg/m{sup 2} irinotecan on days 1, 8, 15, 22, and 29, and 35 mg/m{sup 2} S-1 twice on the day of irradiation (IS group). Curative surgery was performed approximately 4 to 8 weeks after the completion of CRT. The postoperative chemotherapy regimen was FL with a primary endpoint of a pCR rate evaluation. Results: One hundred forty-two eligible patients were randomly assigned, and the median follow-up duration was 43.8 months (95% confidence interval, 40.8-46.8 months). One hundred thirty-three patients (93.7%) of 142 underwent total mesorectal excision; pCR was achieved in 11 (16.7%) of 66 patients in the FL group and 17 (25.8%) of 67 patients in the IS group (P=.246). When good responders were defined as patients with Mandard grades 1 and 2, the rate of good responders was significantly higher in the IS group than in the FL group (54.6% vs 36.4%, respectively, P=.036). The preoperative rates of grade 3 and 4 toxicities were higher in the IS group (7.0%) than in the FL group (1.4%, P=.095). The 3-year disease-free survival was not significantly different between the 2 groups (79.7% vs 76.6%, respectively, P=.896). Conclusions: IS-based preoperative CRT did not increase pCR rate, but it did increase acute toxicities compared with standard 5-FU treatment. Therefore, further investigation is needed.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest

Purpose: To develop an automated system for mediastinal lymph node detection and station mapping for chest CT. Methods: The contextual organs, trachea, lungs, and spine are first automatically identified to locate the region of interest (ROI) (mediastinum). The authors employ shape features derived from Hessian analysis, local object scale, and circular transformation that are computed per voxel in the ROI. Eight more anatomical structures are simultaneously segmented by multiatlas label fusion. Spatial priors are defined as the relative multidimensional distance vectors corresponding to each structure. Intensity, shape, and spatial prior features are integrated and parsed by a random forest classifier for lymph node detection. The detected candidates are then segmented by the following curve evolution process. Texture features are computed on the segmented lymph nodes and a support vector machine committee is used for final classification. For lymph node station labeling, based on the segmentation results of the above anatomical structures, the textual definitions of mediastinal lymph node map according to the International Association for the Study of Lung Cancer are converted into patient-specific color-coded CT image, where the lymph node station can be automatically assigned for each detected node. Results: The chest CT volumes from 70 patients with 316 enlarged mediastinal lymph nodes are used for validation. For lymph node detection, their system achieves 88% sensitivity at eight false positives per patient. For lymph node station labeling, 84.5% of lymph nodes are correctly assigned to their stations. Conclusions: Multiple-channel shape, intensity, and spatial prior features aggregated by a random forest classifier improve mediastinal lymph node detection on chest CT. Using the location information of segmented anatomic structures from the multiatlas formulation enables accurate identification of lymph node stations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Stochastic Galerkin methods for the steady-state Navier–Stokes equations

We study the steady-state Navier–Stokes equations in the context of stochastic finite element discretizations. Specifically, we assume that the viscosity is a random field given in the form of a generalized polynomial chaos expansion. For the resulting stochastic problem, we formulate the model and linearization schemes using Picard and Newton iterations in the framework of the stochastic Galerkin method, and we explore properties of the resulting stochastic solutions. We also propose a preconditioner for solving the linear systems of equations arising at each step of the stochastic (Galerkin) nonlinear iteration and demonstrate its effectiveness for solving a set of benchmark problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

Subcell resolution in simplex stochastic collocation for spatial discontinuities

Subcell resolution has been used in the Finite Volume Method (FVM) to obtain accurate approximations of discontinuities in the physical space. Stochastic methods are usually based on local adaptivity for resolving discontinuities in the stochastic dimensions. However, the adaptive refinement in the probability space is ineffective in the non-intrusive uncertainty quantification framework, if the stochastic discontinuity is caused by a discontinuity in the physical space with a random location. The dependence of the discontinuity location in the probability space on the spatial coordinates then results in a staircase approximation of the statistics, which leads to first-order error convergence and an underprediction of the maximum standard deviation. To avoid these problems, we introduce subcell resolution into the Simplex Stochastic Collocation (SSC) method for obtaining a truly discontinuous representation of random spatial discontinuities in the interior of the cells discretizing the probability space. The presented SSC–SR method is based on resolving the discontinuity location in the probability space explicitly as function of the spatial coordinates and extending the stochastic response surface approximations up to the predicted discontinuity location. The applications to a linear advection problem, the inviscid Burgers’ equation, a shock tube problem, and the transonic flow over the RAE 2822 airfoil show that SSC–SR resolves random spatial discontinuities with multiple stochastic and spatial dimensions accurately using a minimal number of samples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

A fingerprint based metric for measuring similarities of crystalline structures

Measuring similarities/dissimilarities between atomic structures is important for the exploration of potential energy landscapes. However, the cell vectors together with the coordinates of the atoms, which are generally used to describe periodic systems, are quantities not directly suitable as fingerprints to distinguish structures. Based on a characterization of the local environment of all atoms in a cell, we introduce crystal fingerprints that can be calculated easily and define configurational distances between crystalline structures that satisfy the mathematical properties of a metric. This distance between two configurations is a measure of their similarity/dissimilarity and it allows in particular to distinguish structures. The new method can be a useful tool within various energy landscape exploration schemes, such as minima hopping, random search, swarm intelligence algorithms, and high-throughput screenings.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Detecting outbreaks using a spatial latent field

In this paper, we present a method for estimating the infection-rate of a disease as a spatial-temporal field. Our data comprises time-series case-counts of symptomatic patients in various areal units of a region. We extend an epidemiological model, originally designed for a single areal unit, to accommodate multiple units. The field estimation is framed within a Bayesian context, utilizing a parameterized Gaussian random field as a spatial prior. We apply an adaptive Markov chain Monte Carlo method to sample the posterior distribution of the model parameters condition on COVID-19 case-count data from three adjacent counties in New Mexico, USA. Our results suggest that the correlation between epidemiological dynamics in neighboring regions helps regularize estimations in areas with high variance (i.e., poor quality) data. Using the calibrated epidemic model, we forecast the infection-rate over each areal unit and develop a simple anomaly detector to signal new epidemic waves. Our findings show that anomaly detector based on estimated infection-rates outperforms a conventional algorithm that relies solely on case-counts.

Safta, Cosmin [Sandia National Laboratories (SNL-C↗
DOE OSTI2020

Characterization of Extreme Wave Conditions for Wave Energy Converter Design and Project Risk Assessment

Best practices and international standards for determining n-year return period extreme wave (sea states) conditions allow wave energy converter designers and project developers the option to apply simple univariate or more complex bivariate extreme value analysis methods. The present study compares extreme sea state estimates derived from univariate and bivariate methods and investigates the performance of spectral wave models for predicting extreme sea states at buoy locations within several regional wave climates along the US East and West Coasts. Two common third-generation spectral wave models are evaluated, a WAVEWATCH III® model with a grid resolution of 4 arc-minutes (6–7 km), and a Simulating WAves Nearshore model, with a coastal resolution of 200–300 m. Both models are used to generate multi-year hindcasts, from which extreme sea state statistics used for wave conditions characterization can be derived and compared to those based on in-situ observations at National Data Buoy Center stations. Comparison of results using different univariate and bivariate methods from the same data source indicates reasonable agreement on average. Discrepancies are predominantly random. Large discrepancies are common and increase with return period. There is a systematic underbias for extreme significant wave heights derived from model hindcasts compared to those derived from buoy measurements. This underbias is dependent on model spatial resolution. However, simple linear corrections can effectively compensate for this bias. A similar approach is not possible for correcting model-derived environmental contours, but other methods, e.g., machine learning, should be explored.

16 TIDAL AND WAVE POWER↗
DOE OSTI2016

Image Guided Planning for Prostate Carcinomas With Incorporation of Anti-3-[18F]FACBC (Fluciclovine) Positron Emission Tomography: Workflow and Initial Findings From a Randomized Trial

Purpose: {sup 18}F-Fluciclovine (anti-1-amino-3-[{sup 18}F]fluorocyclobutane-1-carboxylic acid) is a novel positron emission tomography (PET)/computed tomography (CT) radiotracer that has demonstrated utility for detection of prostate cancer. Our goal is to report the initial results from a randomized controlled trial of the integration of {sup 18}F-fluciclovine PET-CT into treatment planning for defining prostate bed and lymph node target volumes. Methods and Materials: We report our initial findings from a cohort of 41 patients, of the first enrolled on a randomized controlled trial, who were randomized to the {sup 18}F-fluciclovine arm. All patients underwent {sup 18}F-fluciclovine PET-CT for the detection of metabolic abnormalities and high-resolution CT for treatment planning. The 2 datasets were registered first by use of a rigid registration. If soft tissue displacement was observable, the rigid registration was improved with a deformable registration. Each {sup 18}F-fluciclovine abnormality was segmented as a percentage of the maximum standard uptake value (SUV) within a small region of interest around the lesion. The percentage best describing the SUV falloff was integrated in planning by expanding standard target volumes with the PET abnormality. Results: In 21 of 55 abnormalities, a deformable registration was needed to map the {sup 18}F-fluciclovine activity into the simulation CT. The most selected percentage was 50% of maximum SUV, although values ranging from 15% to 70% were used for specific patients, illustrating the need for a per-patient selection of a threshold SUV value. The inclusion of {sup 18}F-fluciclovine changed the planning volumes for 46 abnormalities (83%) of the total 55, with 28 (51%) located in the lymph nodes, 11 (20%) in the prostate bed, 10 (18%) in the prostate, and 6 (11%) in the seminal vesicles. Only 9 PET abnormalities were fully contained in the standard target volumes based on the CT-based segmentations and did not necessitate expansion. Conclusions: The use of {sup 18}F-fluciclovine in postprostatectomy radiation therapy planning was feasible and led to augmentation of the target volumes in the majority (30 of 41) of the patients studied.

62 RADIOLOGY AND NUCLEAR MEDICINE↗