Deep learning for presumed probability density function models
Not Available
T. Henry de Frahan, Marc↗
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Supercapacitive properties of hydrothermally synthesized sphere like MoS{sub 2} nanostructures
Highlights: • MoS{sub 2} nanostructures were synthesized by hydrothermal method. • Randomly stacked MoS{sub 2} was obtained. • FE-SEM studies show the sphere like morphology of MoS{sub 2}. • Specific capacitance of 92.85 F/g was achieved using charge–discharge analysis. • MoS{sub 2} electrode shows capacitance retention of about 93.8% after 1000 cycles. - Abstract: In this communication, we have investigated the supercapacitive behaviour of MoS{sub 2} nanostructures prepared by a facile one-pot hydrothermal approach using ammonium heptamolybdate and thiourea as starting materials. The X-ray diffraction study revealed the formation of randomly stacked layers of MoS{sub 2}. The field-emission scanning electron microscope studies suggested the formation of sphere like MoS{sub 2} nanostructures and a plausible mechanism for the formation of the obtained structure is discussed. The cyclic voltammetry study shows the typical rectangular shaped curves with a specific capacitance of 106 F/g at a scan rate of 5 mV/s. Galvanostatic charge–discharge measurements suggested the maximum specific capacitance of about 92.85 F/g at discharge current density of 0.5 mA/cm{sup 2}. Cyclic stability tests revealed the capacitance retention of about 93.8% after 1000 cycles suggesting a good cyclic capacity of the prepared MoS{sub 2}. The electrochemical impedance spectroscopic results such as Nyquist and Bode phase angle plots suggested that the hydrothermally synthesized MoS{sub 2} nanostructures will be a suitable candidate for electrochemical supercapacitor applications.
36 MATERIALS SCIENCE↗
Improving Prediction of Surface Solar Irradiance Variability by Integrating Observed Cloud Characteristics and Machine Learning
A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.
Riihimaki, Laura↗
A Practical Comparison of Data-Driven Prognostics Methods for Energy Systems
This study explores data-driven prognostics for nuclear power plant (NPP) condensers, focusing on tube fouling. We utilized the Asherah nuclear power plant simulator (ANS) to compare four methods: Random Forest (RF), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory Neural Network (LSTM). By simulating various fouling scenarios in the ANS, we generated data with different degradation rates under transient operations. The models were trained and tested on these data, with performance evaluated visually and numerically including uncertainty assessment. The LSTM model excelled, exhibiting minimal prediction noise and the most accurate remaining useful life estimates across all degradation levels. Its ability to capture long-term dependencies and produce cleaner outputs makes it a strong candidate, although accurate training data across the entire component lifespan are crucial. The RF model emerged as a robust alternative, providing reliable predictions with high confidence. The FCNN and SVR models, while less effective overall, showed potential under specific conditions. FCNN offers a less complex alternative to LSTM and might benefit from larger datasets. SVR excels in precision when the quality of the training data is high. Furthermore, this study highlights the operational benefits of advanced prognostics in the energy sector and emphasizes the need for further research in NPP condenser health management through real-life experiments.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
ML-Based Power System Stability Assessment Considering Network Topology Changes: WECC 20,000+ Bus System Case Study
Modern power grids are fast-changing and thus require real-time monitoring and online stability assessment. With the rapid development of machine learning (ML) techniques, using data-driven models to provide fast and accurate estimations of power system stability marginal information, such as frequency nadir for frequency stability and critical clearing time (CCT) for transient stability, have become possible. However, despite the numerous research on ML-based methods for frequency nadir and CCT prediction, there is limited work on the impact of different network topology changes. Furthermore, most previous studies only focused on small or synthetic systems, and there is a lack of research on actual large power system models. In this paper, the above issues are addressed by studying the actual U.S. Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. Massive simulations are conducted in PowerWorld Simulator to study the impact of various topology change scenarios on both frequency stability and transient stability. System operating information is extracted from the success dispatch cases of various network topologies to generate a comprehensive dataset for ML-based models. Two ML methods, random forest (RF) and multilayer perceptron (MLP) neural network, are trained and tested for both frequency nadir prediction and CCT prediction. Test results have proven the models are capable of online stability assessment for large power networks such as the WECC system with sufficient accuracy.
critical clearing time↗
The Efficacy and Safety of Mesalamine and Probiotics in Mild‐to‐Moderate Ulcerative Colitis: A Systematic Review and Meta‐Analysis
Objective . To evaluate the efficacy and safety of mesalamine in conjunction with probiotics for ulcerative colitis. Methods . Random controlled trials (RCTs) were searched in PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure, Wanfang, and VIP (VIP Database for Chinese Technical Periodicals) from inception to October 2019. Methodological quality was assessed by the Cochrane Collaboration tool. The quality of evidence was rated by the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE). Data analysis was carried out in Review Manager 5.3. Results . A total of fifteen studies met the criteria for inclusion. Thirteen studies reported the clinical efficacy, three studies provided data on the clinical symptom scores, two trials reported disease activity index, four studies evaluated endoscopic score, and twelve studies reported adverse events. For ulcerative colitis (UC), mesalamine and probiotics had better clinical efficacy than mesalamine alone (≤8 weeks: RR = 1.12, 95% CI: 1.07–1.18, P < 0.0001; >8 weeks: RR = 1.25, 95% CI: 1.11–1.41, P = 0.0003). On the clinical symptom scores, disease activity index, and endoscopic score, UC patients receiving mesalamine and probiotics had significant difference than patients receiving mesalazine alone (MD = −2.02, 95% CI: −3.28 to −0.76, P = 0.002; MD = −1.20, 95% CI: −1.76 to −0.65, P < 0.001; and MD = −0.42, 95% CI: −0.61 to −0.23, P < 0.0001, respectively). There was no statistically significant difference in adverse events between the two groups (RR = 0.88, 95% CI: 0.54 to 1.43, P = 0.60). Conclusion . Our meta‐analysis results supported that mesalamine and probiotics were effective and safe in treating ulcerative colitis.
Tian, Chunying↗
Application of the Combined Judgmental and Randomly Placed Sampling Method
In the wake of the 2001 anthrax incidents in the United States, there has been considerable discussion about the role of judgmental and probabilistic sampling strategies, particularly in the context of clearance sampling. The Combined Judgmental and Random (CJR) methodology provides a statistical methodology that allows practitioners to leverage information from both judgmental and randomly selected sample locations and then quantify the confidence in negative results. The objective of the CJR sampling design is to demonstrate, with high probability, that a high percentage of the decision area (or population) is acceptable, given that none of the samples are unacceptable. The purpose of this document is to assist practitioners to appropriately use the CJR methodology and to implement this sampling design in Visual Sample Plan (VSP). We begin by defining some fundamental terms. A decision area is the geographical or spatial area (indoor or outdoor) about which practitioners wish to draw a conclusion on the basis of sampling and other relevant information. Decision areas typically include all or part of a building, but they may be any contaminated (or potentially contaminated) area for which characterization or clearance is required. In this report, a statistical sampling approach refers to any sampling approach based on a mathematical or statistical model that provides confidence or probability statements about the decision area. Statistical sampling approaches typically involve taking samples at random locations, or at the nodes of a randomly located grid or lattice that is superimposed on the decision area. The term probabilistic sampling is often used to describe statistical sampling designs that exclusively use randomly located samples. This terminology implies that probabilistic statements can only be made based upon the results of randomly placed samples. However, the CJR method utilizes a Bayesian model that gives rise to a formal probabilistic statement regarding the acceptability of the decision area based upon the information obtained from both judgmental and random samples. Judgmental samples play a vital role in the characterization and clearance process because they include the informed prior belief and expert knowledge of personnel involved in the sampling process.
97 MATHEMATICS AND COMPUTING↗
Machine Learning Methods for Connection RTT and Loss Rate Estimation Using MPI Measurements Under Random Losses
Scientific computations are expected to be increasingly distributed across wide-area networks, and Message Passing Interface (MPI) has been shown to scale to support their communications over long distances. Application-level measurements of MPI operations reflect the connection Round-Trip Time (RTT) and loss rate, and machine learning methods have been previously developed to estimate them under deterministic periodic losses. In this paper, we consider more complex, random losses with unform, Poisson and Gaussian distributions. We study five disparate machine leaning methods, with linear and non-linear, and smooth and non-smooth properties, to estimate RTT and loss rate over 10Gbps connections with 0-366ms RTT. The diversity and complexity of these estimators combined with the randomness of losses and TCP’s non-linear response together rule out the selection of a single best among them; instead, we fuse them to retain their design diversity. Overall, the results show that accurate estimates can be generated at low loss rates but become inaccurate at loss rates 10% and higher, thereby illustrating both their strengths and limitations.
Generalization Bounds↗
Reduced basis ANOVA methods for partial differential equations with high-dimensional random inputs
In this paper we present a reduced basis ANOVA approach for partial deferential equations (PDEs) with random inputs. The ANOVA method combined with stochastic collocation methods provides model reduction in high-dimensional parameter space through decomposing high-dimensional inputs into unions of low-dimensional inputs. In this work, to further reduce the computational cost, we investigate spatial low-rank structures in the ANOVA-collocation method, and develop efficient spatial model reduction techniques using hierarchically generated reduced bases. We present a general mathematical framework of the methodology, validate its accuracy and demonstrate its efficiency with numerical experiments.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
A two-level stochastic collocation method for semilinear elliptic equations with random coefficients
Not Available
Chen, Luoping↗
Cluster-based generalized multiscale finite element method for elliptic PDEs with random coefficients
Not provided.
Computer Science↗
Evaluating the performance of random forest and iterative random forest based methods when applied to gene expression data
Gene-to-gene networks, such as Gene Regulatory Networks (GRN) and Predictive Expression Networks (PEN) capture relationships between genes and are beneficial for use in downstream biological analyses. There exists multiple network inference tools to produce these gene-to-gene networks from matrices of gene expression data. Random Forest-Leave One Out Prediction (RF-LOOP) is a method that has been shown to be efficient at producing these gene-to-gene networks, frequently known as GEne Network Inference with Ensemble of trees (GENIE3). Random Forest can be replaced in this process by iterative Random Forest (iRF), which performs variable selection and boosting. Here we validate that iterative Random Forest-Leave One Out Prediction (iRF-LOOP) produces higher quality networks than GENIE3 (RF-LOOP). We use both synthetic and empirical networks from the Dialogue for Reverse Engineering Assessment and Methods (DREAM) Challenges by Sage Bionetworks, as well as two additional empirical networks created from Arabidopsis thaliana and Populus trichocarpa expression data.
59 BASIC BIOLOGICAL SCIENCES↗
Evaluating the Performance of Random Forest and Iterative Random Forest Based Methods when Applied to Gene Expression Data
Gene-to-gene networks, such as Gene Regulatory Networks (GRN) and Predictive Expression Networks (PEN) capture relationships between genes and are beneficial for use in downstream biological analyses. There exists multiple network inference tools to produce these gene-to-gene networks from matrices of gene expression data. Random Forest-Leave One Out Prediction (RF-LOOP) is a method that has been shown to be efficient at producing these gene-to-gene networks, frequently known as GEne Network Inference with Ensemble of trees (GENIE3). Here we validate that iterative Random Forest-Leave One Out Prediction (iRF-LOOP) produces higher quality networks than GENIE3. We use both synthetic and empirical networks from the Dialogue for Reverse Engineering Assessment and Methods (DREAM) Challenges by Sage Bionetworks, as well as two additional empirical networks created from Arabidopsis thaliana and Populus trichocarpa expression data.
iRF-Loop, expression network, Populus Trichocarpa↗
Randomized Federated Learning Methods for Nonsmooth, Nonconvex, and Hierarchical Optimization (Final Technical Report)
This final technical report summarizes the outcomes of a DOE-funded project on federated scientific machine learning (FL) under nonsmooth, nonconvex, and hierarchical optimization settings. The project develops new mathematical models, algorithms, and theoretical guarantees for decentralized stochastic, bilevel, and minimax optimization problems arising in DOE mission-relevant applications. A unified framework of randomized and zeroth-order federated optimization methods is introduced, providing provable convergence, communication efficiency, and sample-complexity guarantees. The report documents algorithmic design, theoretical analysis, and empirical validation of the proposed federated learning methods. The project also contributes to workforce development through graduate training and dissemination of results via publications and seminars.
97 MATHEMATICS AND COMPUTING↗
A minimalistic approach to static and dynamic electron correlations: Amending generalized valence bond method with extended random phase approximation correlation correction
A perfect-pairing generalized valence bond (GVB) approximation is known to be one of the simplest approximations, which allows one to capture the essence of static correlation in molecular systems. In spite of its attractive feature of being relatively computationally efficient, this approximation misses a large portion of dynamic correlation and does not offer sufficient accuracy to be generally useful for studying electronic structure of molecules. We propose to correct the GVB model and alleviate some of its deficiencies by amending it with the correlation energy correction derived from the recently formulated extended random phase approximation (ERPA). On the examples of systems of diverse electronic structures, we show that the resulting ERPA-GVB method greatly improves upon the GVB model. ERPA-GVB recovers most of the electron correlation and it yields energy barrier heights of excellent accuracy. Thanks to a balanced treatment of static and dynamic correlation, ERPA-GVB stays reliable when one moves from systems dominated by dynamic electron correlation to those for which the static correlation comes into play.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
MULTILEVEL ACCELERATION OF STOCHASTIC COLLOCATION METHODS FOR PDE WITH RANDOM INPUT DATA
Stochastic Collocation (SC) methods for stochastic partial differential equa- tions (SPDEs) suffer from the curse of dimensionality, whereby increases in the stochastic dimension cause an explosion of computational effort. To combat these challenges, multilevel approximation methods seek to decrease computational complexity by balancing spatial and stochastic discretization errors. As a form of variance reduction, multilevel techniques have been successfully applied to Monte Carlo (MC) methods, but may be extended to accelerate other methods for SPDEs in which the stochastic and spatial degrees of freedom are de- coupled. This article presents general convergence and computational complexity analysis of a multilevel method for SPDEs, demonstrating its advantages with regard to standard, single level approximation. The numerical results will highlight conditions under which multilevel sparse grid SC is preferable to the more traditional MC and SC approaches.
Webster, Clayton G↗