SIAM CSE 2025 presentation
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McQuarrie, Shane Alexander [Sandia National Labora↗
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Evaluation of CSE modem for aircraft terminal multiple access to communications/navigational satellites Final report
Development of experimental linear swept frequency modulator for aircraft satellite communications
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The effects of chromospheric radiation on the circumstellar chemistry of evolved stars
Astrophysical situations are discussed in which radiation sources other than interstellar are important, and the atomic abundances of the Alpha Ori circumstellar envelope (CSE) are related to the chromospheric and interstellar radiation fields, and to the temperature distribution. The most common Alpha Ori CSE species are neutral atoms and first ions, and the electron fraction is high throughout the entire CSE. A peak in the neutral abundances in the outer CSE appears close to where the chromospheric and interstellar radiation fields are equal. The present theory predicts that the slope of the K I density should change from about -1.5 to -3.5 in the outer envelope, with the mass loss rate implied by this K I density being of the order of 4 x 10 to the -6th solar masses/yr.
Glassgold, A. E.↗
Molecular ions in the circumstellar envelope of IRC + 10216
Assuming a steady and spherically symmetric mass loss, the spatial distribution of the ionization in a C-rich circumstellar envelope (CSE) is calculated, and it is shown that molecular ions dominate the ionization and chemistry of a large part of the CSE. Cosmic ray ionization of molecular hydrogen yields H3(+), and the photoionization of acetylene and neutral carbon yields C2H2(+) and C(+). In the system, the primary ions generate a series of heavier molecular ions by reactions with neutral molecules before being destroyed by dissociative recombination. The most promising candidate for mm-wavelength detection is found to be HCO(+), and IRC + 10216 antenna temperatures are calculated for different telescope sizes. Photoproduced C2H2(+), the most abundant intermediate distance CSE ion, may be responsible for the synthesis of various heavy hydrocarbon molecules at levels observed in IRC + 10216.
Glassgold, A. E.↗
Detection of HCN and C2H2 in ISO Spectra of Oxygen-Rich AGB Stars
Cool oxygen-rich AGB stars were not expected to have organic molecules like HCN in either their photospheres or circumstellar envelopes (CSEs). The discovery of HCN and CS microwave emission from the shallowest CSE layers of these stars was a considerable surprise and much theoretical effort has been expended in explaining the presence of such organics. To further explore this problem, we have undertaken a systematic search of oxygen-rich AGB stellar spectra in the Infrared Space Observatory (ISO) data archive. Our purposes are to find evidence regarding critical molecular species that could be of value in choosing among the proposed theoretical models, to locate spectral features which might give clues to conditions deeper in the CSEs, and to lay the groundwork for future SIRTF (Space Infrared Telescope Facility) and SOFIA (Stratospheric Observatory for Infrared Astronomy) observations. Using carefully reduced observations, we have detected weak absorption features arising from HCN and possibly C2H2 in a small number of oxygen-rich AGB stars. The most compelling case is NML Cyg which shows both HCN (14 microns) and CO2 (15 microns). VY CMa, a similar star, shows evidence for HCN, but not CO2. Two S-type stars show evidence for the C-H bending transitions: W Aql at 14 microns (HCN) and both W Aql and S Cas at 13.7 microns (C2H2). Both W Aql and S Cas as well as S Lyr, a SC-type star, show 3 micron absorption which may arise from the C-H stretch of HCN and C2H2. In the case of NML Cyg, we show that the HCN and CO2 spectral features are formed in the CSE at temperatures well above those of the outermost CSE layers and derive approximate column densities. In the case of the S-stars, we discuss the evidence for the organic features and their photospheric origin.
Carbon, Duane F.↗
Towards Test Driven Development for Computational Science with pFUnit
Developers working in Computational Science & Engineering (CSE)/High Performance Computing (HPC) must contend with constant change due to advances in computing technology and science. Test Driven Development (TDD) is a methodology that mitigates software development risks due to change at the cost of adding comprehensive and continuous testing to the development process. Testing frameworks tailored for CSE/HPC, like pFUnit, can lower the barriers to such testing, yet CSE software faces unique constraints foreign to the broader software engineering community. Effective testing of numerical software requires a comprehensive suite of oracles, i.e., use cases with known answers, as well as robust estimates for the unavoidable numerical errors associated with implementation with finite-precision arithmetic. At first glance these concerns often seem exceedingly challenging or even insurmountable for real-world scientific applications. However, we argue that this common perception is incorrect and driven by (1) a conflation between model validation and software verification and (2) the general tendency in the scientific community to develop relatively coarse-grained, large procedures that compound numerous algorithmic steps.We believe TDD can be applied routinely to numerical software if developers pursue fine-grained implementations that permit testing, neatly side-stepping concerns about needing nontrivial oracles as well as the accumulation of errors. We present an example of a successful, complex legacy CSE/HPC code whose development process shares some aspects with TDD, which we contrast with current and potential capabilities. A mix of our proposed methodology and framework support should enable everyday use of TDD by CSE-expert developers.
pFUnit↗
Selective Amnesia using Contrastive Subnet Erasure for Class Level Unlearning in Vision Models
We study concept-level forgetting in pretrained vision models: removing an entire semantic category so the system no longer recognizes that object in unseen images and contexts, rather than merely forgetting specific training examples. Prior work either applies blunt global projections or fine-tunes parameters, which can introduce collateral damage to unrelated features, add compute, and become unstable as forgetting strength increases. We introduce Contrastive Subnet Erasure (CSE), a training-free, encoder-centric edit that targets a compact set of channels most responsible for the class and attenuates them in a calibrated manner. The modification is algebraically folded into the subsequent layer, yielding no inference-time overhead and leaving task heads unchanged. To evaluate whether forgetting generalizes beyond the data used to specify the class, we introduce a cross dataset protocol in which the class is defined on a source dataset and performance is measured on a disjoint target dataset drawn from a different distribution with no shared images. This setup tests whether the model still fails to recognize the object when it looks different or appears in new scenes, and it helps avoid overfitting to patterns in the source dataset. Across CIFAR 10, CIFAR 100, and ImageNet under this protocol, CSE achieves stronger forgetting of the target class while better preserving non target utility than existing baselines in both single class and multi class settings. Overall, CSE provides a simple, stable, and deployment-ready mechanism for class-level unlearning in vision.
Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗
Experimental and Coupled-channels Investigation of the Radiative Properties of the N2 c4 (sup 1)Sigma+(sub u) - X (sup 1)Sigma+(sub g) Band System
The emission properties of the N2 c(sup prime)(sub 4) (sup 1)Sigma+(sub u) - Chi (sup 1)Sigma+(sub g) band system have been investigated in a joint experimental and coupled-channels theoretical study. Relative intensities of the c(sup prime)(sub 4) (sup 1)Sigma+(sub u)(0) - Chi (sup 1)Sigma+(sub g)(v(sub i)) transitions, measured via electron-impact-induced emission spectroscopy, are combined with a coupled-channel Schroedinger equation (CSE) model of the N2 molecule, enabling determination of the diabatic electronic transition moment for the c(sup prime)(sub 4) (sup 1)Sigma+(sub u) - Chi (sup 1)Sigma+(sub g) system as a function of internuclear distance. The CSE probabilities are further verified by comparison with a high-resolution experimental spectrum. Spontaneous transition probabilities of the c(sup prime)(sub 4) (sup 1)Sigma+(sub u) - Chi (sup 1)Sigma+(sub g) modeling atmospheric emission, can now be calculated reliably.
internuclear distance↗
2019 Budget Request for the DOE Computational Science Graduate Fellowship (CSGF) Grant
The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is necessary to meet the continual challenging national workforce needs that arise as computational science and engineering problems continue to grow in scope and complexity. Computational science and engineering (CSE) is a multidisciplinary approach that uses scientific computing to solve practical problems methods and to supply technical tools across the scientific discovery spectrum. In particular, the DOE CSGF emphasizes high-performance computing (HPC) that enables CSE that advances science and engineering in directions important to the DOE and the economy in general. Over the past half-century, HPC has been an essential tool for DOE’s success. During this period, important missions, such as nuclear stockpile stewardship, have turned to HPC as an essential technology. Entire science disciplines, such as biology and cosmology, have been transformed through the augmentation of scientific observation via HPC. At government laboratories and in industry, DOE CSGF alumni are helping push traditional HPC boundaries while contributing to discoveries in high-energy physics, renewable energy, fusion-reactor design, additive manufacturing, nanomaterials for next-generation batteries and transistors, and turbine and advanced nuclear reactor modeling. In addition, HPC is used to address national health needs that will eventually point to cures both by helping cancer researchers manage and analyze huge troves of data, by simulating biological mechanisms, and by accelerating drug development — including continuing to rise to the challenge of pandemic-related research. A 2023 report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR office, “Can the United States Maintain Its Leadership in High-Performance Computing?” says of the Program, “The CSGF program provides a barometer for disciplines that will be of interest to future DOE computing.” An explosion in scientific and technological data has driven the need for increasingly sophisticated HPC to transform those data into scientific understanding. With access to more and more data and the proliferation of HPC, Machine Learning and Artificial Intelligence are experiencing a renaissance, complementing the now well-established use of computational simulation. Indeed, in its September 2020 subcommittee report on “AI/ML, Data Intensive Science and High-Performance Computing”, the DOE Advanced Scientific Computing Advisory Committee (ASCAC) explicitly called for a fellowship program to train computational and data scientists to tackle exascale and data-intensive computing challenges. This collaboration of empirical and theory-based modeling will increasingly inform federal policymakers whose decisions affect American society and future generations, and it requires highly skilled and intellectually agile computational scientists who can support the fast-moving DOE National Laboratory research environment. In fact, the DOE CSGF program has explicitly and consistently addressed this need.
97 MATHEMATICS AND COMPUTING↗
2020 Budget Request for the DOE Computational Science Graduate Fellowship (CSGF) Grant
The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is essential for addressing the increasingly complex national workforce demands stemming from the growth of computational science and engineering challenges. Computational science and engineering (CSE) takes a multidisciplinary approach that utilizes scientific computing to tackle practical problems and provide technical tools across the spectrum of scientific discovery. The DOE CSGF specifically highlights high-performance computing (HPC) as a critical enabling technology in CSE, driving advancements in science and engineering that are vital to both the DOE and the broader economy. Over the past half-century, HPC has been an essential tool for DOE’s success. During this period, important missions, such as nuclear stockpile stewardship, have turned to HPC as an essential technology. Entire science disciplines have been transformed through the augmentation of scientific observation via HPC. At government laboratories, academic institutions, and in industry, DOE CSGF alumni are helping push traditional HPC boundaries while contributing to discoveries in high-energy physics, quantum information systems, fusion-reactor design, machine learning, additive manufacturing, nano materials for next-generation batteries and transistors, and advanced nuclear reactor modeling. In addition, HPC is used to address national health needs that will eventually point to cures both by helping cancer researchers manage and analyze huge troves of data, by simulating biological mechanisms, and by accelerating drug development. A 2023 report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR office, “Can the United States Maintain Its Leadership in High-Performance Computing?” says of the Program, “The CSGF program provides a barometer for disciplines that will be of interest to future DOE computing. Computational biology, machine learning, and quantum computing are among the subjects that began to swell in the ranks of CSGF applicants before the labs were hiring as high a percentage of employees in these categories.” The explosion of scientific and technological data has heightened the demand for advanced high-performance computing (HPC) to transform these data into meaningful scientific insights. As access to vast amounts of data increases, the fields of Machine Learning and Artificial Intelligence are experiencing a resurgence, enhancing the established practices of computational modeling and simulation. In its September 2020 subcommittee report on "AI/ML, Data Intensive Science, and High-Performance Computing," the DOE Advanced Scientific Computing Advisory Committee (ASCAC) specifically called for a fellowship program to train computational and data scientists to address exascale and data-intensive computing challenges. This integration of empirical and theoretical modeling will increasingly guide federal policymakers in making decisions that impact American society and future generations. It demands a workforce of highly skilled and intellectually agile computational scientists capable of navigating the rapid advancements in scientific computing within the DOE National Laboratory research environment. The DOE CSGF program has consistently addressed this critical need.
97 MATHEMATICS AND COMPUTING↗
Microsegregation in directionally solidified Pb-8.4 at. pct Au alloy
The dependence of microsegregation behavior on growth rate and thermal gradient has been examined in a Pb-8.4 at. pct Au alloy material partially directionally solidified and quenched. The composition of the quenched 'liquid' at the dendrite tip (Ct), that of the eutectic-like solid phase freezing from the interdendritic liquid at the base of dendrite (Cse), the volume fraction of this eutectic-like region (fe), and solute profiles in the interdendritic quenched liquid and ahead of the dendrite have been measured. Two dendritic growth models for solidification of a binary alloy melt in a positive thermal gradient at the liquid-solid interface, one for dendrites with 'minimum undercooled dendrite tip' and the other for an Ivantsov type of dendrite with 'marginally stable tip', have been examined for a quantitative comparison with measured values of Ct, Cse, and fe. Convection in the melt, possibly due to horizontal density gradients, is found to be a serious limitation for theoretical understanding of the observed experimental behavior and meaningful comparison of theories.
Tewari, S. N.↗
The outer layers of cool, non-Mira carbon stars
The outer layers and near circumstellar envelope (CSE) of a typical carbon star have been studied using available data from theoretical and empirical models. An attempt is made to match the density-velocity structure of the photosphere-chromosphere region to values from the radio CO observations, which arise from the outer CSE. It is concluded that the stellar atmosphere includes a relatively thin high-temperature region close to hydrostatic equilibrium and a much more extended cooler region of outflowing gas and dust. To extend the outer photosphere and chromosphere to match the mass loss density appears to require an injection of energy and momentum by some mechanism rather close to the stellar surface.
Johnson, H. R.↗
Cooperative high-performance storage in the accelerated strategic computing initiative
The use and acceptance of new high-performance, parallel computing platforms will be impeded by the absence of an infrastructure capable of supporting orders-of-magnitude improvement in hierarchical storage and high-speed I/O (Input/Output). The distribution of these high-performance platforms and supporting infrastructures across a wide-area network further compounds this problem. We describe an architectural design and phased implementation plan for a distributed, Cooperative Storage Environment (CSE) to achieve the necessary performance, user transparency, site autonomy, communication, and security features needed to support the Accelerated Strategic Computing Initiative (ASCI). ASCI is a Department of Energy (DOE) program attempting to apply terascale platforms and Problem-Solving Environments (PSEs) toward real-world computational modeling and simulation problems. The ASCI mission must be carried out through a unified, multilaboratory effort, and will require highly secure, efficient access to vast amounts of data. The CSE provides a logically simple, geographically distributed, storage infrastructure of semi-autonomous cooperating sites to meet the strategic ASCI PSE goal of highperformance data storage and access at the user desktop.
Gary, Mark↗
Composite Solid Electrolyte for Li Battery Applications
The electrochemical, bulk and interfacial properties of the polyethylene oxide (PEO) based composite solid electrolyte (CSE) comprising LiI, PEO, and Al2O3 have been evaluated for Li battery applications. The bulk interfacial and transport properties of the CSEs seem to strongly depend on the alumina particle size. For the CSE films with 0.05 micron alumina while the bulk conductivity is around 10(exp -4) (mho/cm) at 103 C, the Li ion transport number seems to be close to unity at the same temperature. Compared to the PEO electrolyte this polymer composite electrolyte seems to exhibit robust mechanical and interfacial properties. We have studied three different films with three different alumina sizes in the range 0.01-0.3 micron. Effects of Al2O3 particle size on the electrochemical performance of polymer composite electrolyte is discussed. With TiS2 as cathode a 10 mAh small capacity cell was charged and discharged at C/40 and C/20 rates respectively.
Nagasubramanian, G.↗
Composite Solid Electrolyte for Li Battery Applications
The electrochemical, bulk and interfacial properties of the polyethylene oxide (PEO) based polymer composite electrolyte (CSE) comprising LiI, PEO, and A1 2 0 3 have been evaluated for Li battery applications.
alumina↗
Lignin’s Indispensable role in orchestrating seed stone formation: Insights from jujuba, peach and pear with future prospective on pitless fruits
A hard endocarp (i.e., stone) inside fruit is a characteristic of drupe fruits such as jujube, peach, mango, etc. Hard stone significantly affects the quality and downstream processing of fruits. The complex aromatic polymer lignin deposition in the secondary cell wall determines stone hardness. Lignin comprises phenylpropanoid units formed by hydroxycinnamoyl alcohol, which includes coniferyl, sinapyl, and p-coumaroyl alcohols. Lignin biosynthesis pathway involves a series of complex enzymatic reactions initiated from phenylalanine ammonia lyase and ends up polymerizing lignin monomers by laccase and peroxidase enzymes. Phytohormones, particularly auxin, gibberellins, and Ca²⁺ signaling, further modulate endocarp lignification by regulating transcriptional networks and lignin biosynthetic genes, thereby fine-tuning secondary cell wall thickening and stone hardness in drupe fruits. Lignin biosynthesis is controlled by both structural genes and transcriptional regulators. The structural genes encoding lignin biosynthetic enzymes include LAC12–1, PAL2, C4H, C3H, CSE, CCoAOMT, F5H, CAD, and PRX1. In addition, several transcription factors regulating secondary cell wall and lignin deposition, such as MYB24, bZIP48, and bZIP33 play key regulatory roles. Conversely, delignification or suppression of stone formation is associated with transcription factors (Pistillata, MYB32, FUL, and REPLUMLESS) and post-transcriptional regulators, including miR397a, miR31-3p, and miR8-5p. Accurate alteration in the expression of these genes will result in the attainment of stoneless fruits for cheap and hazel-free downstream processing.
Fruit endocarp↗
Neural Universal Differential Equation Hypernetwork Surrogates for Agent-Based Disease Models
Presentation for SIAM CSE 2025 on new surrogate modeling for epidemiological agent-based models
Robertson, Connor James [Sandia National Laborator↗
An in-depth analysis and modelling of the Shuttle to MILA S-band telemetry link
The S-Band radio frequency (RF) link between the Merritt Island (MILA) Tracking Station and the Space Shuttle launch pads is a critical communication path for prelaunch and launch operations. The proposed siting of the Center for Space Education (CSE) at the Visitor Center required a study to avoid RF line-of-sight blockage and reflection paths. The study revealed the trees near MILA's 9-meter (9-M) antennas are obstructing the optical line-of-sight. The studies found diffraction is the main propagation mechanism. This paper describes a link model based on the Geometric Theory of Diffraction.
Caroglanian, Armen↗