Supervised Learning in Physical Networks: From Machine Learning to Learning Machines
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36 MATERIALS SCIENCE↗
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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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ExaMPM
ExaMPM is a mini-application for the Material Point Method (MPM) for studying the application of MPM to future exascale computing systems. MPM is a general method for computational mechanics and fluids and is used in a wide variety of science and engineering disciplines to study problems with large deformations, phase change, fracture, and other phenomena. ExaMPM provides a reference implementation of MPM as described in the 1994 work of Sulsky et.al. (Sulsky, Deborah, Zhen Chen, and Howard L. Schreyer. "A particle method for history-dependent materials." Computer methods in applied mechanics and engineering 118.1-2 (1994): 179-196.). The software can solve basic MPM problems in solid mechanics using the original algorithm of Sulsky with explicit time integration, basic geometries, and free-slip and no-slip boundary conditions as described in the reference. ExaMPM is intended to be used as a starting point to design new parallel algorithms for the next generation of DOE supercomputers.
Slattery, StuartR↗
Accelerating Control Systems with GitOps: A Path to Automation and Reliability
GitOps is a foundational approach for modernizing infrastructure by leveraging Git as the single source of truth for declarative configurations. The poster explores how GitOps transforms traditional control system infrastructure, services and applications by enabling fully automated, auditable, and version-controlled infrastructure management. Cloud-native and containerized environments are shifting the ecosystem not only in the IT industry but also within the computational science field, as is the case of CERN and Diamond Light Source among other Accelerator/Science facilities which are slowly shifting towards modern software and infrastructure paradigms. The ACORN project, which aims to modernize Fermilab’s control system infrastructure and software is implementing proven best-practices and cutting-edge technology standards including GitOps, containerization, infrastructure as code and modern data pipelines for control system data acquisition and the inclusion of AI/ML in our accelerator complex.
Gonzalez, M. [Fermilab]↗
Current State, Challenges, and Opportunities in Genome-Scale Resource Allocation Models: A Mathematical Perspective
Stoichiometric genome-scale metabolic models (generally abbreviated GSM, GSMM, or GEM) have had many applications in exploring phenotypes and guiding metabolic engineering interventions. Nevertheless, these models and predictions thereof can become limited as they do not directly account for protein cost, enzyme kinetics, and cell surface or volume proteome limitations. Lack of such mechanistic detail could lead to overly optimistic predictions and engineered strains. Initial efforts to correct these deficiencies were by the application of precursor tools for GSMs, such as flux balance analysis with molecular crowding. In the past decade, several frameworks have been introduced to incorporate proteome-related limitations using a genome-scale stoichiometric model as the reconstruction basis, which herein are called resource allocation models (RAMs). This review provides a broad overview of representative or commonly used existing RAM frameworks. This review discusses increasingly complex models, beginning with stoichiometric models to precursor to RAM frameworks to existing RAM frameworks. RAM frameworks are broadly divided into two categories: coarse-grained and fine-grained, with different strengths and challenges. Discussion includes pinpointing their utility, data needs, highlighting framework strengths and limitations, and appropriateness to various research endeavors, largely through contrasting their mathematical frameworks. Finally, promising future applications of RAMs are discussed.
59 BASIC BIOLOGICAL SCIENCES↗
Diffusion Codes: Self-Correction from Small(er)-Set Expansion with Tunable Non-locality
Optimal constructions of classical LDPC codes can be obtained by choosing the Tanner graph uniformly at random among biregular graphs. We introduce a class of codes that we call ``diffusion codes'', defined by placing each edge connecting bits and checks on some graph, and acting on that graph with a random SWAP network. By tuning the depth of the SWAP network, we can tune a tradeoff between the amount of randomness -- and hence the optimality of code parameters -- and locality with respect to the underlying graph. For diffusion codes defined on the cycle graph, if the SWAP network has depth $\sim Tn$ with $T> n^{2β}$ for arbitrary $β>0$, then we prove that almost surely the Tanner graph is a lossless ``smaller set'' vertex expander for small sets up size $δ\sim \sqrt T \sim n^β$, with bounded bit and check degree. At the same time, the geometric size of the largest stabilizer is bounded by $\sqrt T$ in graph distance. We argue, based on physical intuition, that this result should hold more generally on arbitrary graphs. By taking hypergraph products of these classical codes we obtain quantum LDPC codes defined on the torus with smaller-set boundary and co-boundary expansion and the same expansion/locality tradeoffs as for the classical codes. These codes are self-correcting and admit single-shot decoding, while having the geometric size of the stabilizer growing as an arbitrarily small power law. Our proof technique establishes mixing of a random SWAP network on small subsystems at times scaling with only the subsystem size, which may be of independent interest.
Combinatorics (math.CO)↗
ExaMPM
ExaMPM is a mini-application for the Material Point Method (MPM) for studying the application of MPM to future exascale computing systems. MPM is a general method for computational mechanics and fluids and is used in a wide variety of science and engineering disciplines to study problems with large deformations, phase change, fracture, and other phenomena. ExaMPM provides a reference implementation of MPM as described in the 1994 work of Sulsky et.al. (Sulsky, Deborah, Zhen Chen, and Howard L. Schreyer. "A particle method for history-dependent materials." Computer methods in applied mechanics and engineering 118.1-2 (1994): 179-196.). The software can solve basic MPM problems in solid mechanics using the original algorithm of Sulsky with explicit time integration, basic geometries, and free-slip and no-slip boundary conditions as described in the reference. ExaMPM is intended to be used as a starting point to design new parallel algorithms for the next generation of DOE supercomputers.
Slattery, Stuart R↗
Building Virtual Ecosystems: Computational Challenges for Mechanistic Modeling of Terrestrial Environments Workshop, Germantown, Maryland, March 26–27, 2014
The mission of the Climate and Environmental Sciences Division of the Office of Biological and Environmental Research (BER) within the U.S. Department of Energy’s (DOE) Office of Science is “to advance a robust, predictive understanding of Earth’s climate and environmental systems and to inform the development of sustainable solutions to the nation’s energy and environmental challenges.” This formidable challenge requires quantification of stocks and controls on states, fluxes, and residence times of water, carbon, and other key elements through all components of the terrestrial system. These components include vegetation, soils, the deep vadose zone, groundwater, and surface water. To achieve this level of predictive understanding, a new generation of multiscale, multiphysics models is needed for terrestrial systems—models that incorporate process couplings and feedbacks between various “pools” (i.e., vegetation, soils, subsurface aquifers, and surface waters) across wide ranges of spatial and temporal scales. To explore the potential of a new generation of multiscale, multiphysics models for revolutionizing the understanding of terrestrial ecosystem dynamics, BER held the Computational Challenges for Mechanistic Modeling of Terrestrial Environments workshop on March 26–27, 2014, in Germantown, Maryland. The workshop brought together 29 researchers with diverse expertise, including hydrologists, environmental scientists, ecologists, microbiologists, plant scientists, and computational scientists. Through a combination of invited talks, breakout sessions, and report-back discussions, workshop attendees identified challenges and research opportunities needed to develop a more seamless and continuous framework for mechanistic modeling of terrestrial environments extending from the bedrock to the atmospheric boundary layer and from single-plant systems to fields of crops to watersheds and river basins.
54 ENVIRONMENTAL SCIENCES↗
Subspace Density Matrix Functional Embedding Theory: Theory, Implementation, and Applications to Molecular Systems
We introduce the subspace density matrix functional embedding theory (sDMFET), in which optimization of the nonlocal embedding potential and subsequent embedded correlated wave function calculations are carried out within a truncated subspace determined by a Schmidt decomposition. As compared to the original density matrix functional embedding theory [K. Yu and E. A. Carter, Proceedings of the National Academy of Sciences 2017, 114, E10861], the computational cost of sDMFET is significantly reduced while the accuracy is preserved. Here, we perform test calculations for both covalently and noncovalently bound molecular systems to demonstrate the feasibility of our theory.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
TECA: Petascale pattern recognition for climate science
Climate Change is one of the most pressing challenges facing humanity in the 21st century. Climate simulations provide us with a unique opportunity to examine effects of anthropogenic emissions. Highresolution climate simulations produce “Big Data”: contemporary climate archives are ≈ 5PB in size and we expect future archives to measure on the order of Exa-Bytes. In this work, we present the successful application of TECA (Toolkit for Extreme Climate Analysis) framework, for extracting extreme weather patterns such as Tropical Cyclones, Atmospheric Rivers and Extra-Tropical Cyclones from TB-sized simulation datasets. TECA has been run at full-scale on Cray XE6 and IBM BG/Q systems, and has reduced the runtime for pattern detection tasks from years to hours. TECA has been utilized to evaluate the performance of various computational models in reproducing the statistics of extreme weather events, and for characterizing the change in frequency of storm systems in the future.
54 ENVIRONMENTAL SCIENCES↗
The experimental nuclear reaction data (EXFOR): Extended computer database and Web retrieval system
The EXchange FORmat (EXFOR) experimental nuclear reaction database and the associated Web interface provide access to the wealth of low- and intermediate-energy nuclear reaction physics data. This resource is based on numerical data sets and bibliographical information of ~22,000 experiments since the beginning of nuclear science. The principles of the computer database organization, its extended contents and Web applications development are described. New capabilities for the data sets uploads, renormalization, covariance matrix, and inverse reaction calculations are presented in this paper. The EXFOR database, updated monthly, provides an essential support for nuclear data evaluation, application development, and research activities. Finally, it is publicly available at the websites of the International Atomic Energy Agency Nuclear Data Section, http://www-nds.iaea.org/exfor, the U.S. National Nuclear Data Center, http://www.nndc.bnl.gov/exfor, and the mirror sites in China, India and Russian Federation.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Climbing the Summit and Pushing the Frontier of Mixed Precision Benchmarks at Extreme Scale
The rise of machine learning (ML) applications and their use of mixed precision to perform interesting science are driving forces behind AI for science on HPC. The convergence of ML and HPC with mixed precision offers the possibility of transformational changes in computational science. The HPL-AI benchmark is designed to measure the performance of mixed precision arithmetic as opposed to the HPL benchmark which measures double precision performance. Pushing the limits of systems at extreme scale is nontrivial -little public literature explores optimization of mixed precision computations at this scale. In this work, we demonstrate how to scale up the HPL-AI benchmark on the pre-exascale Summit and exascale Frontier systems at the Oak Ridge Leadership Computing Facility (OLCF) with a cross-platform design. We present the implementation, performance results, and a guideline of optimization strategies employed for delivering portable performance on both AMD and NVIDIA GPUs at extreme scale.
Lu, Hao↗
Competitive binding of STATs to receptor phospho-Tyr motifs accounts for altered cytokine responses
Cytokines elicit pleiotropic and non-redundant activities despite strong overlap in their usage of receptors, JAKs and STATs molecules. We use IL-6 and IL-27 to ask how two cytokines activating the same signaling pathway have different biological roles. We found that IL-27 induces more sustained STAT1 phosphorylation than IL-6, with the two cytokines inducing comparable levels of STAT3 phosphorylation. Mathematical and statistical modeling of IL-6 and IL-27 signaling identified STAT3 binding to GP130, and STAT1 binding to IL-27Rα, as the main dynamical processes contributing to sustained pSTAT1 levels by IL-27. Mutation of Tyr613 on IL-27Rα decreased IL-27-induced STAT1 phosphorylation by 80% but had limited effect on STAT3 phosphorgylation. Strong receptor/STAT coupling by IL-27 initiated a unique gene expression program, which required sustained STAT1 phosphorylation and IRF1 expression and was enriched in classical Interferon Stimulated Genes. Interestingly, the STAT/receptor coupling exhibited by IL-6/IL-27 was altered in patients with systemic lupus erythematosus (SLE). IL-6/IL-27 induced a more potent STAT1 activation in SLE patients than in healthy controls, which correlated with higher STAT1 expression in these patients. Partial inhibition of JAK activation by sub-saturating doses of Tofacitinib specifically lowered the levels of STAT1 activation by IL-6. Our data show that receptor and STATs concentrations critically contribute to shape cytokine responses and generate functional pleiotropy in health and disease.
59 BASIC BIOLOGICAL SCIENCES↗
Isothermal evaporation process simulation using the Pitzer model for the Quinary system LiCl–NaCl–KCl–SrCl 2 –H 2 O at 298.15 K
In this study, the Pitzer thermodynamic model for solid-liquid equilibria in the quinary system LiCl–NaCl–KCl–SrCl 2 –H 2 O at 298.15 K was constructed by selecting the proper parameters for the subsystems in the literature. The solubility data of the systems NaCl–SrCl 2 –H 2 O, KCl–SrCl 2 –H 2 O, LiCl–SrCl 2 –H 2 O, and NaCl–KCl–SrCl 2 –H 2 O were used to evaluate the model. Good agreement between the experimental and calculated solubilities shows that the model is reliable. The Pitzer model for the quinary system at 298.15 K was then used to calculate the component solubilities and conduct computer simulation of isothermal evaporation of the mother liquor for the oilfield brine from Nanyishan district in the Qaidam Basin. The evaporation-crystallization path and sequence of salt precipitation, change in concentration and precipitation of lithium, sodium, potassium, and strontium, and water activities during the evaporation process were demonstrated. The salts precipitated from the brine in the order : KCl, NaCl, SrCl 2 ∙6H 2 O, SrCl 2 ∙2H 2 O, and LiCl∙H 2 O. The entire evaporation process may be divided into six stages. In each stage the variation trends for the relationships between ion concentrations or water activities and the evaporation ratio are different. This result of the simulation of brines can be used as a theoretical reference for comprehensive exploitation and utilization of this type of brine resources.
36 MATERIALS SCIENCE↗
SU-F-T-94: Plan2pdf - a Software Tool for Automatic Plan Report for Philips Pinnacle TPS
Purpose: To implement an automatic electronic PDF plan reporting tool for Philips Pinnacle treatment planning system (TPS) Methods: An electronic treatment plan reporting software is developed by us to enable fully automatic PDF report from Pinnacle TPS to external EMR programs such as MOSAIQ. The tool is named “plan2pdf”. plan2pdf is implemented using Pinnacle scripts, Java and UNIX shell scripts, without any external program needed. plan2pdf supports full auto-mode and manual mode reporting. In full auto-mode, with a single mouse click, plan2pdf will generate a detailed Pinnacle plan report in PDF format, which includes customizable cover page, Pinnacle plan summary, orthogonal views through each plan POI and maximum dose point, DRR for each beam, serial transverse views captured throughout the dose grid at a user specified interval, DVH and scorecard windows. The final PDF report is also automatically bookmarked for each section above for convenient plan review. The final PDF report can either be saved on a user specified folder on Pinnacle, or it can be automatically exported to an EMR import folder via a user configured FTP service. In manual capture mode, plan2pdf allows users to capture any Pinnacle plan by full screen, individual window or rectangular ROI drawn on screen. Furthermore, to avoid possible patients’ plan mix-up during auto-mode reporting, a user conflict check feature is included in plan2pdf: it prompts user to wait if another patient is being exported by plan2pdf by another user. Results: plan2pdf is tested extensively and successfully at our institution consists of 5 centers, 15 dosimetrists and 10 physicists, running Pinnacle version 9.10 on Enterprise servers. Conclusion: plan2pdf provides a highly efficient, user friendly and clinical proven platform for all Philips Pinnacle users, to generate a detailed plan report in PDF format for external EMR systems.
60 APPLIED LIFE SCIENCES↗
High-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory
Metal oxide-based Resistive Random-Access Memory (RRAM) exhibits multiple resistance states, arising from the activation/deactivation of a conductive filament (CF) inside a switching layer. Understanding CF formation kinetics is critical to achieving optimal functionality of RRAM. Here a phase-field model is developed, based on materials properties determined by ab initio calculations, to investigate the role of electrical bias, heat transport and defect-induced Vegard strain in the resistive switching behavior, using MO 2- x systems such as HfO 2- x as a prototypical model system. It successfully captures the CF formation and resultant bipolar resistive switching characteristics. High-throughput simulations are performed for RRAMs with different material parameters to establish a dataset, based on which a compressed-sensing machine learning is conducted to derive interpretable analytical models for device performance (current on/off ratio and switching time) metrics in terms of key material parameters (electrical and thermal conductivities, Vegard strain coefficients). These analytical models reveal that optimal performance (i.e., high current on/off ratio and low switching time) can be achieved in materials with a low Lorenz number, a fundamental material constant. This work provides a fundamental understanding to the resistive switching in RRAM and demonstrates a computational data-driven methodology of materials selection for improved RRAM performance, which can also be applied to other electro-thermo-mechanical systems.
36 MATERIALS SCIENCE↗
Computationally Tractable High-Fidelity Representation of Global Hydrology in ESMs via Machine Learning Approaches to Scale-Bridging
Focal Areas: This paper responds primarily to Focal Area 2, focusing on AI techniques to improve model fidelity. Science Challenge: “Hyperresolution” [1, 2] land surface models (LSMs) running at far higher resolution than typically employed in global Earth system models (ESMs) can help answer critical questions about the water cycle and associated ecosystem and biogeochemical feedbacks. Even with all foreseeable advances in computing power and efficient solver algorithms, however, employing hyperresolution LSMs inside ESMs for studies of long-term global climate is not computationally feasible. Instead, we argue for incorporating the fidelity of hyperresolution LSMs only where and when it is needed by using machine learning approaches to scale-bridging.
54 ENVIRONMENTAL SCIENCES↗
NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering
Automation and optimization of chemical systems require well-informed decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. Here we introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian optimization algorithms and supports both automated an d human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A Four-Layer Cyber-Physical Security Model for Electric Machine Drives Considering Control Information Flow
Despite the IEEE Power Electronics Society (PELS) establishing Technical Committee 10 on Design Methodologies with a focus on the cyber-physical security of power electronics systems, a holistic design methodology for addressing security vulnerabilities remains underdeveloped. This gap largely stems from the limited integration of computer science and power/control engineering studies in this interdisciplinary field. Addressing the inadequacy of unilateral cyber or control perspectives, this article presents a novel four-layer cyber-physical security model specifically designed for electric machine drives. Central to this model is the innovative control information flow (CIF) model, residing within the control layer, which serves as a pivotal link between the cyber layer's vulnerable resources and the physical layer's state-space models. By mapping vulnerable resources to control variable space and tracing attack propagation, the CIF model facilitates accurate impact predictions based on tainted control laws. The effectiveness and validity of this proposed model are demonstrated through hardware experiments involving two typical cyber-attack scenarios, underscoring its potential as a comprehensive framework for multidisciplinary security strategies.
97 MATHEMATICS AND COMPUTING↗