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Focal Area: (2) Predictive modeling using AI techniques and AI-derived model components; use of AI and other tools to design a prediction system comprising of a hierarchy of models
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NVRAM-based Burst Buffers are an important part of the emerging HPC storage landscape. The National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory recently installed one of the first Burst Buffer systems as part of its new Cori supercomputer, collaborating with Cray on the development of the DataWarp software. NERSC has a diverse user base comprised of over 6500 users in 700 different projects spanning a wide variety of scientific computing applications. The use-cases of the Burst Buffer at NERSC are therefore also considerable and diverse. We describe here performance measurements and lessons learned from the Burst Buffer Early User Program at NERSC, which selected a number of research projects to gain early access to the Burst Buffer and exercise its capability to enable new scientific advancements. To the best of our knowledge this is the first time a Burst Buffer has been stressed at scale by diverse, real user workloads and therefore these lessons will be of considerable benefit to shaping the developing use of Burst Buffers at HPC centers.
We consider the current state of a completely new area of science; microsystem electromechanics. Тhe wide spectra of its practical applications and prospects for further development are analyzed. We discuss in detail two main directions of creating micro- and nanoelectromechanical converters as basic elements of microsystem electromechanics: “top down” and “down top.” We also describe the main technological methods in constructing basic functional elements of microsystem electromechanics, areas of their application in traditional and new technologies (information and computer technologies, medicine, aerospace, rocket and artillery systems, etc.). At the same time, we discuss some key issues of generalized physical and mathematical modeling of microelectromechanical and nanoelectromechanical systems (MEMS and NEMS). A new generalized approach is proposed to study dynamic and energy characteristics of MEMS and NEMS as complex dynamic systems with binary conjugate subsystems. Based on proposed theoretical principles and models, the possibilities of studying electrophysical characteristics of biological nanostructures have been considered.
The project entitled, "Nonlinear Modeling of Macroscopic Plasma Dynamics," applied numerical computation to solve mathematical models of plasma behavior in laboratory systems. Plasma is the state of matter with sufficiently large internal energy density such that electrons dissociate from atoms to form gas of electrically charged particles. It occurs naturally in stars, planetary magnetospheres, and interstellar and intergalactic media. It is also used for industrial purposes and in lighting. Fusion energy science seeks to create and sustain plasma conditions where atomic nuclei fuse to create energy that can be captured for power production. Many laboratory experiments for this effort use magnetic field to contain plasma and to thermally insulate it from its surroundings. Possible plasma dynamics in magnetized systems include collective behavior over spatial scales that are comparable to the configuration size, and these dynamics often include evolution of the magnetic field that is used for confinement. Solving the mathematical models that describe these dynamics in specific experiments often requires computer simulations, as done in this project. This project analyzed plasma magnetic relaxation dynamics in the laboratory configurations known as the reversed-field pinch (RFP), the spheromak, and the spherical torus (ST). It also developed capabilities for modeling disruptive plasma dynamics in the tokamak, which has achieved the highest performance for magnetized plasma and is the design of the large international ITER experiment (https://www.iter.org) that is under construction and is expected to be the first to achieve self-heating conditions. Dynamic evolution to states of low energy, the aforementioned magnetic relaxation, is known to be important for generating the RFP and spheromak configurations. Nonlinear computational modeling with the Non-Ideal Magnetohydrodynamics with Rotation, Open Discussion code (NIMROD, https://nimrodteam.org) was applied to understand specific aspects of magnetic relaxation in RFPs, spheromaks, and STs. The RFP study considered how the nonlinear transfer of power among different fluctuation harmonics is altered due to the application of pulses of electric field, as done experimentally in the Madison Symmetric Torus RFP device at the University of Wisconsin-Madison. It also investigated the separation of electron and ion dynamics, known as two-fluid effects, including effects on the evolution of the plasma flow profile. The spheromak study considered the influence of these two-fluid effects in conditions relevant to the Sustained Spheromak Experiment (SSPX), formerly at Lawrence Livermore National Laboratory. Our study found that two-fluid effects enhance stability with respect to interchange in SSPX-relevant conditions, but having a complete model with two-fluid effects in separate electron and ion temperature equations is important. The study for STs focused on plasma startup in the Pegasus Toroidal Experiment at UW-Madison, which uses DC voltage applied through a small plasma gun. The simulations show how the resulting helical current stream relaxes through repetitive merging, forming current rings that accumulate into a tokamak-like configuration. Analytical theory was also developed to help explain behavior that was observed in the RFP and spheromak computations. Although the tokamak configuration has outperformed other magnetic confinement configurations, it has a tendency to undergo discharge-terminating dynamics that release energy onto plasma-facing surfaces and exert electromechanical forces on magnetic coils and structures. Our efforts have improved NIMROD's capabilities to model tokamak disruption by incorporating a resistive wall model that can be applied to axisymmetric, as well as asymmetric, distortions of the magnetic field configuration. Computations addressed whether mathematical conditions applied to plasma flow at the wall influence disruptive dynamics.
Abstract For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step toward quantifying parametric uncertainty in Arctic climate, we performed a variance‐based global sensitivity analysis (GSA) using a fully coupled, ultra‐low resolution (ULR) configuration of version 1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interests (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere, and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed preindustrial forcing. Uncertainties in the atmospheric parameters in the Cloud Layers Unified by Binormals (CLUBB) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher‐resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies.
Variations in microbial community structure and metabolic efficiency are governed in part by oxygen availability, which is a function of water content, diffusion distance, and oxygen demand; for this reason, the volume, connectivity, and geometry of soil pores may exert primary controls on spatial metabolic diversity in soil. Here, we combine quantitative pore network metrics derived from X-ray computed tomography (XCT) with measurements of electromotive potentials to assess how the metabolic status of soil depends on variations of the overall pore network architecture. Contrasting pore network architectures were generated using a Mollisol—A horizon, and compared to intact control samples from the same soil. Mesocosms from each structural treatment were instrumented with Pt-electrodes to record available energy dynamics during a regimen of varying moisture conditions. We found that volume-based XCT-metrics were more frequently correlated with metrics describing changes in available energy than medial-axis XCT-metrics. An abundance of significant correlations between pore network metrics and available energy parameters was not only a function of pore architecture, but also of the dimensions of the sub-sample chosen for XCT analysis. Pore network metrics had the greatest power to statistically explain changes in available energy in the smallest volumes analyzed. Our work underscores the importance of scale in observations of natural systems.
Interactions induced by electromagnetic fluctuations, such as van der Waals and Casimir forces, are of universal nature present at any length scale between any types of systems. In such interactions these are important not only for the fundamental science of materials behavior, but also for the design and improvement of micro- and nanostructured devices. In the past decade, many new materials have become available, which has stimulated the need for understanding their dispersive interactions. The field of van der Waals and Casimir forces has experienced an impetus in terms of developing novel theoretical and computational methods to provide new insights into related phenomena. The understanding of such forces has far reaching consequences as it bridges concepts in materials, atomic and molecular physics, condensed-matter physics, high-energy physics, chemistry, and biology. Our review summarizes major breakthroughs and emphasizes the common origin of van der Waals and Casimir interactions. Progress related to novel ab initio modeling approaches and their application in various systems, interactions in materials with Dirac-like spectra, force manipulations through nontrivial boundary conditions, and applications of van der Waals forces in organic and biological matter are examined. Finally, the outlook of the review is to give the scientific community a materials perspective of van der Waals and Casimir phenomena and stimulate the development of experimental techniques and applications.
In this study, we present a new decomposition approach for dual-energy computed tomography (DECT) called SIRZ that provides precise and accurate material description, independent of the scanner, over diagnostic energy ranges (30 to 200 keV). System independence is achieved by explicitly including a scanner-specific spectral description in the decomposition method, and a new X-ray-relevant feature space. The feature space consists of electron density, ρ e , and a new effective atomic number, Z e , which is based on published X-ray cross sections. Reference materials are used in conjunction with the system spectral response so that additional beam-hardening correction is not necessary. The technique is tested against other methods on DECT data of known specimens scanned by diverse spectra and systems. Uncertainties in accuracy and precision are less than 3% and 2% respectively for the (ρ e , Z e ) results compared to prior methods that are inaccurate and imprecise (over 9%).
In single photon emission computed tomography, the choice of the collimator has a major impact on the sensitivity and resolution of the system. Traditional parallel-hole and fan-beam collimators used in clinical practice, for example, have a relatively poor sensitivity and subcentimeter spatial resolution, while in small-animal imaging, pinhole collimators are used to obtain submillimeter resolution and multiple pinholes are often combined to increase sensitivity. This paper reviews methods for production, sensitivity maximization, and task-based optimization of collimation for both clinical and preclinical imaging applications. New opportunities for improved collimation are now arising primarily because of (i) new collimator-production techniques and (ii) detectors with improved intrinsic spatial resolution that have recently become available. These new technologies are expected to impact the design of collimators in the future. The authors also discuss concepts like septal penetration, high-resolution applications, multiplexing, sampling completeness, and adaptive systems, and the authors conclude with an example of an optimization study for a parallel-hole, fan-beam, cone-beam, and multiple-pinhole collimator for different applications.
Deep learning has transformed the use of machine learning technologies for the analysis of large experimental datasets. In science, such datasets are typically generated by large-scale experimental facilities, and machine learning focuses on the identification of patterns, trends and anomalies to extract meaningful scientific insights from the data. In upcoming experimental facilities, such as the Extreme Photonics Application Centre (EPAC) in the UK or the international Square Kilometre Array (SKA), the rate of data generation and the scale of data volumes will increasingly require the use of more automated data analysis. Furthermore, at present, identifying the most appropriate machine learning algorithm for the analysis of any given scientific dataset is a challenge due to the potential applicability of many different machine learning frameworks, computer architectures and machine learning models. Historically, for modelling and simulation on high-performance computing systems, these issues have been addressed through benchmarking computer applications, algorithms and architectures. Extending such a benchmarking approach and identifying metrics for the application of machine learning methods to open, curated scientific datasets is a new challenge for both scientists and computer scientists. Here, we introduce the concept of machine learning benchmarks for science and review existing approaches. As an example, we describe the SciMLBench suite of scientific machine learning benchmarks.
Diffusivity of species and defects on grain boundaries is usually several orders of magnitude larger than that inside grains. Such strongly inhomogeneous diffusivity requires prohibitively high computational demands for modeling microstructural evolution. Here, this paper presents a highly-efficient numerical solver, combining the Finite Difference method and Random Walk model, designed for accurately modeling strongly inhomogeneous diffusion within polycrystalline structures. The proposed solver, termed Finite Difference informed Random Walk (FDiRW), integrates a customized Finite Difference (cFD) scheme tailored for fast diffusion along thin grain boundaries represented by a single-layer of nodes. Numerical experiments demonstrate that the FDiRW solver achieves an impressive efficiency gain of 1560x compared to traditional Finite Difference methods while maintaining accuracy, making it feasible for personal computer machines to handle diffusional systems with strongly inhomogeneous diffusivity across static polycrystalline microstructures. The model has been successfully applied to simulate radiation defect evolution, showcasing its scalability to engineering scales in both length and time dimensions.
With support from the Department of Energy’s Water Power Technologies Office, Pacific Northwest National Laboratory (PNNL) has developed new tools that incorporate cutting-edge climate and hydrological science capabilities to assess the potential long-term impacts of future climate conditions on unregulated streamflow and water temperature within watershed-river-reservoir systems. The objectives of this project were achieved by enhancing key hydrologic and hydrodynamic models and transferring them to a high-performance computing environment to provide a high-spatiotemporal resolution, multi-scale modeling framework. The new modeling framework has the potential to quantify risks related climate change impacts on runoff, unregulated streamflow, and water temperature. Initial development and demonstration of the modeling framework was conducted under historical and future climate conditions in the Columbia River Basin in the Pacific Northwest and the Connecticut River Basin in New England.
Zeolites, having widespread applications in chemical industries, are often synthesized using organic templates. These can be cost-prohibitive, motivating investigations into their role in promoting crystallization. Herein, the relationship between framework structure, chemical composition, synthesis conditions and the conformation of the occluded, economical template tetraethylammonium (TEA + ) has been systematically examined by experimental and computational means. The results show two distinct regimes of occluded conformer tendencies: 1) In frameworks with a large stabilization energy difference, only a single conformer was found (BEA, LTA and MFI). 2) In the frameworks with small stabilization energy differences (AEI, AFI, CHA and MOR), less than the interconversion of TEA + in solution, a heteroatom-dependent (Al, B, Co, Mn, Ti, Zn) distribution of conformers was observed. Our findings demonstrate that host–guest chemistry principles, including electrostatic interactions and coordination chemistry, are as important as ideal pore-filling.
To circumvent the von Neumann bottleneck, substantial progress has been made towards in-memory computing with synaptic devices. However, compact nanodevices implementing non-linear activation functions are required for efficient full-hardware implementation of deep neural networks. Here, in this work, we present an energy-efficient and compact Mott activation neuron based on vanadium dioxide and its successful integration with a conductive bridge random access memory (CBRAM) crossbar array in hardware. The Mott activation neuron implements the rectified linear unit function in the analogue domain. The neuron devices consume substantially less energy and occupy two orders of magnitude smaller area than those of analogue complementary metal–oxide semiconductor implementations. The LeNet-5 network with Mott activation neurons achieves 98.38% accuracy on the MNIST dataset, close to the ideal software accuracy. We perform large-scale image edge detection using the Mott activation neurons integrated with a CBRAM crossbar array. Our findings provide a solution towards large-scale, highly parallel and energy-efficient in-memory computing systems for neural networks.
In this Letter, we introduce FusionNet, a multi-modality deep learning framework designed to predict and analyze output pulses in high-power rare-earth-doped laser systems driving parametric conversion in homogeneous guided nonlinear media. FusionNet integrates temporal, spectral, and physical experimental conditions to model ultrafast nonlinear phenomena, including parametric nonlinear frequency conversion, self-phase modulation, and cross-phase modulation in homogeneous guided systems such as gas-filled hollow-core fibers. These systems bridge physical models with experimental data, advancing our understanding of light-guiding principles and nonlinear interactions while expediting the design and optimization of on-demand high-power, high-brightness systems. Our results demonstrate a 73% reduction in prediction error and an 83% improvement in computational efficiency compared to conventional neural networks. This work establishes a new paradigm for accelerating parametric simulations and optimizing experimental designs in high-power laser systems, with further implications for high-precision spectroscopy, quantum information science, and distributed entangled interconnects.
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We have developed the first transient metabolic model for diurnal growth of algae based on experimental data and capable of predicting phenotype from genotype. This model enables us to evaluate the impact of genetic and environmental changes on the growth, biomass composition and intracellular fluxes of the model green alga, Chlamydomonas reinhardtii . The availability of this model will enable faster and more efficient design of cells for production of fuels, chemicals, and pharmaceuticals.