Information extraction from historical well records using a large language model
Not Available
54 ENVIRONMENTAL SCIENCES↗
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Results for “COMPUTATIONAL SCIENCE”
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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Experimental discovery of structure–property relationships in ferroelectric materials via active learning
Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these functionalities have been discovered and quantified via local scanning probe microscopy methods. However, the search has until now been based on either trial and error, or using auxiliary information such as the topography or domain wall structure to identify potential objects of interest on the basis of the intuition of operator or pre-existing hypotheses, with subsequent manual exploration. Here we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization-switching characteristics in ferroelectric materials encoded in the hysteresis loop. The hysteresis loops and their scalar descriptors such as nucleation bias, coercive bias and the hysteresis loop area (or more complex functionals of hysteresis loop shape) and corresponding uncertainties are used to guide the discovery of these relationships via automated piezoresponse force microscopy and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high-dimensional data, as well as human-based physics insights encoded into the acquisition function. For ferroelectric materials, this automated workflow demonstrates that the discovery path and sampling points of on- and off-field hysteresis loops are largely different, indicating that on- and off-field hysteresis loops are dominated by different mechanisms. Here, the proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.
36 MATERIALS SCIENCE↗
Machine learning of optical properties of materials – predicting spectra from images and images from spectra
Assembling the world's largest materials image and spectroscopy dataset enables training of machine learning models that learn hidden relationships in materials data, providing a key example of the data requirements to capitalize on recent advancements in computer science.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Graeme A. Bird
Few of those who read the 1963 research note by Graeme A. Bird in Physics of Fluids could have imagined that 50 years later the proposed new numerical technique would have become the dominant numerical technique in molecular gas dynamics. The introduction of the Direct Simulation Monte Carlo (DSMC) method not only has altered the field of molecular gas dynamics but also has influenced fields such as physical chemistry, mathematics, computer science, and aerothermodynamics. Further, the DSMC method has been used to probe into previously uninvestigated theoretical aspects of the Boltzmann equation and has also served as a platform for the development of nonequilibrium chemistry models. The DSMC method’s most noteworthy achievement is that molecular gas dynamics became a practical tool in the hands of aerospace engineers in many situations of spacecraft design and mission analysis.
99 GENERAL AND MISCELLANEOUS↗
Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water–methane dimer
Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely trusted many-body method for solving the Schrödinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability with system complexity and near-perfect utilization of computational power make FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated. This difficulty stems from the diverse array of DMC algorithms and trial wave functions, compounded by the method’s inherent stochastic nature. Here, this study represents a community-wide effort to assess the reproducibility of the method, affirming that yes, FN-DMC is reproducible (when handled with care). Using the water–methane dimer as the canonical test case, we compare results from eleven different FN-DMC codes and show that the approximations to treat the non-locality of pseudopotentials are the primary source of the discrepancies between them. In particular, we demonstrate that, for the same choice of determinantal component in the trial wave function, reliable and reproducible predictions can be achieved by employing the T-move, the determinant locality approximation, or the determinant T-move schemes, while the older locality approximation leads to considerable variability in results. These findings demonstrate that, with appropriate choices of algorithmic details, fixed-node DMC is reproducible across diverse community codes—highlighting the maturity and robustness of the method as a tool for open and reliable computational science.
Della Pia, Flaviano [Univ. of Cambridge (United Ki↗
Transformational Challenge Reactor Safety Design and Radionuclide Retention Strategy
An integrated safety design and radionuclide (RN) retention strategy is developed to support the Transformational Challenge Reactor (TCR) demonstration. This demonstration aims to showcase viability for rapid deployment of a novel reactor by leveraging the advances in materials, manufacturing, and computational sciences through a highly integrated and agile design and development approach. This strategy provides a logical description and understanding of how RNs are contained within the facility. Rather than discussing fission product barriers individually between separate design and safety basis reports, this paper provides a consistent description and narrative to better facilitate regulatory interactions and focus safety design efforts. The principal barriers credited include the various coating layers in the tristructural isotropic (TRISO) fuel particle, the silicon carbide (SiC) matrix hosting the particles within the fuel element, the helium pressure boundary, and the confinement system. The choice and assumed performance of the credited barriers are highly conservative, which is a direct reflection of the low hazard that the TCR demonstration presents and the need to simplify and focus the safety review process accordingly. However, the strategy and the associated framework are generalized and may be adopted and tailored to support other advanced reactor demonstration efforts.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Modeling the role of preferential snow accumulation in through talik development and hillslope groundwater flow in a transitional permafrost landscape
Not Available
54 ENVIRONMENTAL SCIENCES↗
Design of a Do-It-Yourself Geiger-Muller Counter With Smartphone Mapping Application
Legacy Geiger-Muller (GM) survey meters recovered from fallout shelters have been used by several nuclear scientific societies as part of high school outreach programs. A donated antique instrument helps teachers demonstrate radiological principles, but fails to develop student's electronics skills, generate excitement for nuclear careers, or provide individuals with their own devices to explore the radioactive planet. A simple, affordable GM survey meter built by each student would increase direct engagement while providing hands-on experience with circuit-building, soldering, and computer programming. The inclusion of an affordable single-board computer as a component in the survey meter would enable students to tackle more various computer science and electronics projects, thereby potentially recruiting more students into technology and engineering. This paper details the challenges faced by an interdisciplinary undergraduate team designing an easy-to-assemble smart GM survey meter. Their iterative research, design, and testing process included modification to a basic circuit to enable use of different tube types, component cost reduction, application development, and data communication. The ultimate product of the team's efforts, a survey meter with affordable components and a smartphone application capable of creating radiation maps, is detailed in full.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Congruity of genomic and epidemiological data in modelling of local cholera outbreaks
Cholera continues to be a global health threat. Understanding how cholera spreads between locations is fundamental to the rational, evidence-based design of intervention and control efforts. Traditionally, cholera transmission models have used cholera case-count data. More recently, whole-genome sequence data have qualitatively described cholera transmission. Integrating these data streams may provide much more accurate models of cholera spread; however, no systematic analyses have been performed so far to compare traditional case-count models to the phylodynamic models from genomic data for cholera transmission. Here, we use high-fidelity case-count and whole-genome sequencing data from the 1991 to 1998 cholera epidemic in Argentina to directly compare the epidemiological model parameters estimated from these two data sources. We find that phylodynamic methods applied to cholera genomics data provide comparable estimates that are in line with established methods. Our methodology represents a critical step in building a framework for integrating case-count and genomic data sources for cholera epidemiology and other bacterial pathogens.
59 BASIC BIOLOGICAL SCIENCES↗
Glassy Word Problems: Ultraslow Relaxation, Hilbert Space Jamming, and Computational Complexity
We introduce a family of local models of dynamics based on “word problems” from computer science and group theory, for which we can place rigorous lower bounds on relaxation timescales. These models can be regarded either as random circuit or local Hamiltonian dynamics and include many familiar examples of constrained dynamics as special cases. The configuration space of these models splits into dynamically disconnected sectors, and for initial states to relax, they must “work out” the other states in the sector to which they belong. When this problem has a high time complexity, relaxation is slow. In some of the cases we study, this problem also has high space complexity. When the space complexity is larger than the system size, an unconventional type of jamming transition can occur, whereby a system of a fixed size is not ergodic but can be made ergodic by appending a large reservoir of sites in a trivial product state. This finding manifests itself in a new type of Hilbert space fragmentation that we call fragile fragmentation. We present explicit examples where slow relaxation and jamming strongly modify the hydrodynamics of conserved densities. In one example, density modulations of wave vector q exhibit almost no relaxation until times O ( exp ( 1 / q ) ) , at which point they abruptly collapse. We also comment on extensions of our results to higher dimensions. Published by the American Physical Society 2024
Physics↗
Evaluating the Observability of Network Security Monitoring Strategies With TOMATO
Not Available
42 ENGINEERING↗
Semantic Segmentation of Micrographs for Nuclear Fuel Analysis and Degradation Quantification
Not Available
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Analyzing the Impact of System Reliability Events on Applications in the Titan Supercomputer
Extreme-scale computing systems employ Reliability, Availability and Serviceability (RAS) mechanisms and infrastructure to log events from multiple system components. In this paper, we analyze RAS logs in conjunction with the application placement and scheduling database, in order to understand the impact of common RAS events on application performance. This study conducted on the records of about 2 million applications executed on Titan supercomputer provides important insights for system users, operators and computer science researchers. Specifically, we investigate the impact of RAS events on application performance and its variability by comparing cases where events are recorded with corresponding cases where no events are recorded. Such a statistical investigation is possible since we observed that system users tend to execute their applications multiple times. Our analysis reveals that most RAS events do impact application performance, although not always. We also find that different system components affect application performance differently. In particular, our investigation includes the following components: parallel file system processor, memory, graphics processing units, system and user software issues. Our work establishes the importance of providing feedback to system users for increasing operational efficiency of extreme-scale systems.
Ashraf, Rizwan↗
AHEAD: A Tool for Projecting Next-Generation Hardware Enhancements on GPU-Accelerated Systems
Starting with the Titan supercomputer (at the Oak Ridge Leadership Computing Facility, OLCF) in 2012, top supercomputers have Increasingly leveraged the performance of GPUs to support large-scale computational science. The current No. 1 machine, the 200 petaflop Summit system at OLCF, is a GPU-based machine. Accelerator-based architectures, however, add additional complexity due to node heterogeneity. To inform procurement decisions, supercomputing centers need the tools to quickly model the impact of changes of the node architectures on application performance. We present AHEAD, a profiling and modeling tool to quantify the impact of intra-node communication mechanism (e.g., PCI or NVLink) on application performance. Our experiments show average weighted relative errors of ~19% and ~23% for five CORAL-2 (a collaboration between multiple US Department of Energy, DOE, labs to procure Exascale systems) and 12 Rodinia benchmarks respectively, without running the applications on the target future node.
Abdelhafez, Hazem A.↗
Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources
Applications which fuse machine learning and simulation are rarely best served by a single computing resource. Highly parallel simulation codes are best deployed on super- computers, while AI tasks used to decide which simulations to perform may be best suited to specialized accelerators. Here we present a Function-as-a-Service (FaaS) system for executing complex, distributed computational campaigns that achieves performance parity with conventional workflow systems without the complexities of secure network connections between compute providers. One innovation enabling high performance is a subsystem that directly moves task data between sites, separate from the cloud-hosted FaaS system used to distribute task instructions. We also introduce a flexible scheduling system that allows us access factor of 2 trade offs between the amount of resources required to solve a problem at each compute site. We anticipate that this system will upgrade multi-site applications from demonstration projects to routine practice in computational science.
Ward, Logan↗
Real-World Cyber Security Demonstration for Networked Electric Drives
In this article, we present the design and implementation of a cyber-physical security testbed for networked electric drive systems, aimed at conducting real-world security demonstrations. To our knowledge, this is one of the first security testbeds for networked electric drives, seamlessly integrating the domains of power electronics and computer science, and cybersecurity. By doing so, the testbed offers a comprehensive platform to explore and understand the intricate and often complex interactions between cyber and physical systems. The core of our testbed consists of four electric machine drives, meticulously configured to emulate small-scale but realistic information technology (IT) and operational technology (OT) networks. This setup both provides a controlled environment for simulating a wide array of cyber-attacks, and mirrors potential real-world attack scenarios with a high degree of fidelity. The testbed serves as an invaluable resource for the study of cyber-physical security, offering a practical and dynamic platform for testing and validating cybersecurity measures in the context of networked electric drive systems. As a concrete example of the testbed's capabilities, we have developed and implemented a Python-based script designed to execute step-stone attacks over a wireless local area network (WLAN). This script leverages a sequence of target IP addresses, simulating a real-world attack vector that could be exploited by adversaries. To counteract such threats, we demonstrate the efficacy of our developed cyber-attack detection algorithms, which are integral to our testbed's security framework. Furthermore, the testbed incorporates a real-time visualization system using InfluxDB and Grafana, providing a dynamic and interactive representation of networked electric drives and their associated security monitoring mechanisms. This visualization component not only enhances the testbed's usability but also offers insightful, real-time data for researchers and practitioners, thereby facilitating a deeper understanding of cyber-physical security dynamics in networked electric drive systems.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Bi-fidelity Gradient-Based Approach for Nonlinear Well Logging Inverse Problems
Solving a non-linear inverse problem is challenging in computational science and engineering. Sampling based methods require a large number of model valuations; gradientbased methods require fewer model evaluations but only find the local minima. Multifidelity optimization combines the low fidelity model and the high-fidelity model to achieve both high accuracy and high efficiency. In this paper, we present a bi-fidelity approach to solve non-linear inverse problems. In the bi-fidelity inversion method, the low-fidelity model is used to acquire a good initial guess, and the high-fidelity model is used to locate the global minimum. Combined with a multi-start optimization scheme, the proposed approach significantly increases the possibility of finding the global minimum for nonlinear inverse problems with many local minima. The method is tested with two toy problems and then applied to an electromagnetic well logging inverse problem, which is difficult to solve using traditional gradient-based methods. The bi-fidelity method provides promising inversion results and can be easily applied to traditional gradient-based methods.
42 ENGINEERING↗
The Ongoing Evolution of OpenMP
Here, this paper presents an overview of the past, present and future of the OpenMP application programming interface (API). While the API originally specified a small set of directives that guided shared memory fork-join parallelization of loops and program sections, OpenMP now provides a richer set of directives that capture a wide range of parallelization strategies that are not strictly limited to shared memory. As we look toward the future of OpenMP, we immediately see further evolution of the support for that range of parallelization strategies and the addition of direct support for debugging and performance analysis tools. Looking beyond the next major release of the specification of the OpenMP API, we expect the specification eventually to include support for more parallelization strategies and to embrace closer integration into its Fortran, C and, in particular, C++ base languages, which will likely require the API to adopt additional programming abstractions.
97 MATHEMATICS AND COMPUTING↗