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arXiv 2021年

作者： Lan, Ingrid S. Liu, Ju Yang, Weiguang Marsden, Alison L. Department of Bioengineering Stanford University StanfordCA94305 United States Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and Technology Guangdong Shenzhen518055 China Stanford University StanfordCA94305 United States Institute for Computational and Mathematical Engineering Stanford University StanfordCA94305 United States

We recently derived the unified continuum and variational multiscale formulation for fluid-structure interaction (FSI) using the Gibbs free energy as the thermodynamic potential. Restricting our attention to vascular FSI, we now reduce this formulation in arbitrary Lagrangian-Eulerian (ALE) coordinates by adopting three common modeling assumptions for the vascular wall. The resulting semi-discrete formulation, referred to as the reduced unified continuum formulation, achieves monolithic coupling of the FSI system in the Eulerian frame through a simple modification of the fluid boundary integral. While ostensibly similar to the semi-discrete formulation of the coupled momentum method introduced by Figueroa et al., its underlying derivation does not rely on an assumption of a fictitious body force in the elastodynamics sub-problem and therefore represents a direct simplification of the ALE method. Furthermore, uniform temporal discretization of the entire FSI system is performed via the generalized-α scheme. In contrast to the predominant approach yielding only first-order accuracy for pressure, we collocate both pressure and velocity at the intermediate time step to achieve uniform second-order temporal accuracy. In conjunction with quadratic tetrahedral elements, our methodology offers higher-order temporal and spatial accuracy for quantities of clinical interest, including pressure and wall shear stress. Furthermore, without loss of consistency, a segregated predictor multi-corrector algorithm is developed to preserve the same block structure as for the incompressible Navier-Stokes equations in the implicit solver’s associated linear system. Block preconditioning of a monolithically coupled FSI system is therefore made possible for the first time. Compared to alternative preconditioners, our three-level nested block preconditioner, which achieves improved representation of the Schur complement, demonstrates robust performance over a wide range of physical parameter

关键词： Shear stress

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Nuclear Neural Networks: Emulating Late Burning Stages in Core Collapse Supernova Progenitors

arXiv

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arXiv 2025年

作者： Grichener, Aldana Renzo, Mathieu Kerzendorf, Wolfgang E. Farmer, Rob de Mink, Selma E. Bellinger, Earl Patrick Chan, Chi-Kwan Chen, Nutan Farag, Ebraheem Justham, Stephen Steward Steward Observatory Department of Astronomy University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Max Planck Institute for Astrophysics Karl-Schwarzschild-Str. 1 Garching85748 Germany Department of Physics Technion Haifa3200003 Israel Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Ludwig-Maximilians-Universitat Munchen Geschwister-Scholl-Platz 1 Munchen80539 Germany Department of Astronomy Yale University New HavenCT06511 United States Steward Observatory Department of Astronomy University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Avenue TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 North Santa Rita TucsonAZ85721 United States Machine Learning Research Lab Volkswagen AG Munich38440 Germany

One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of fully-coupled stiff ordinary differential equations (ODEs), making the simulations computationally intensive and prone to numerical instability. To overcome this barrier, we design a nuclear neural network (NNN) framework with multiple hidden layers to emulate nucleosynthesis calculations and conduct a proof-of-concept to evaluate its performance. The NNN takes the temperature, density and composition of a burning region as input and predicts the resulting isotopic abundances along with the energy generation and loss rates. We generate training sets for initial conditions corresponding to oxygen core depletion and beyond using large nuclear reaction networks, and compare the predictions of the NNNs to results from a commonly used small net. We find that the NNNs improve the accuracy of the electron fraction by 280 − 660 % and the nuclear energy generation by 250 − 750 %, consistently outperforming the small network across all timesteps. They also achieve significantly better predictions of neutrino losses on relatively short timescales, with improvements ranging from 100 − 106 %. While further work is needed to enhance their accuracy and applicability to different stellar conditions, integrating NNN trained models into stellar evolution codes is promising for facilitating large-scale generation of core-collapse supernova (CCSN) progenitors with higher physical fidelity. © 2025, CC BY.

关键词： Nucleosynthesis

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Robust charge-density wave correlations in the electron-doped single-band Hubbard model

arXiv

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arXiv 2022年

作者： Mai, Peizhi Nichols, Nathan S. Karakuzu, Seher Bao, Feng Maestro, Adrian Del Maier, Thomas A. Johnston, Steven Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831-6494 United States Department of Physics Institute of Condensed Matter Theory University of Illinois at Urbana-Champaign UrbanaIL61801 United States Data Science and Learning Division Argonne National Laboratory ArgonneIL60439 United States Center for Computational Quantum Physics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Mathematics Florida State University TallahasseeFL32306 United States Department of Physics and Astronomy The University of Tennessee KnoxvilleTN37996 United States Institute of Advanced Materials and Manufacturing The University of Tennessee KnoxvilleTN37996 United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States

There is growing evidence that the hole-doped single-band Hubbard and t-J models do not have a superconducting ground state reflective of the high-temperature cuprate superconductors but instead have striped spin- and charge-ordered ground states. Nevertheless, it is proposed that these models may still provide an effective low-energy model for electron-doped materials. Here we study the finite temperature spin and charge correlations in the electron-doped Hubbard model using quantum Monte Carlo dynamical cluster approximation calculations and contrast their behavior with those found on the hole-doped side of the phase diagram. We find evidence for a charge modulation with both checkerboard and unidirectional components decoupled from any spin-density modulations. These correlations are inconsistent with a weak-coupling description based on Fermi surface nesting, and their doping dependence agrees qualitatively with resonant inelastic x-ray scattering measurements. Our results provide evidence that the single-band Hubbard model describes the electron-doped cuprates. © 2022, CC BY.

关键词： Ground state

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An Information-Theoretic Framework for Optimal Experimental Design in Magnetic Nanoparticle Hyperthermia

SSRN

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SSRN 2025年

作者： Nandyala, Mahesh Lanham, Andrew Jha, Prashant K. Wu, Chengyue Hazle, John D. Yankeelov, Thomas E. Stafford, R. Jason El-Gendy, Ahmed A. Fuentes, David Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX78712 United States Department of Imaging Physics The University of Texas M.D. Anderson Cancer Center HoustonTX77030 United States Applied Research Laboratories The University of Texas at Austin AustinTX78758 United States Department of Mechanical Engineering South Dakota School of Mines and Technology Rapid CitySD57701 United States Department of Breast Imaging The University of Texas M.D. Anderson Cancer Center HoustonTX77030 United States Department of Biostatistics The University of Texas M.D. Anderson Cancer Center HoustonTX77030 United States The Institute for Data Science in Oncology The University of Texas M.D. Anderson Cancer Center HoustonTX77030 United States Department of Biomedical Engineering The University of Texas at Austin AustinTX78712 United States Department of Diagnostic Medicine The University of Texas at Austin AustinTX78712 United States Department of Oncology The University of Texas at Austin AustinTX78712 United States Livestrong Cancer Institutes The University of Texas at Austin AustinTX78712 United States Department of Physics University of Texas at El Paso El PasoTX79968 United States

Magnetic nanoparticle hyperthermia is an emerging cancer therapy that utilizes magnetic nanoparticles subjected to alternating magnetic fields to generate localized heating and selectively target tumor tissues. Despite its potential, clinical implementation faces significant challenges due to uncertainties in the thermophysical properties, nanoparticle distribution, and magnetic particle parameters, which can compromise the precision and efficacy of the treatment. This article introduces an information-theoretic framework for optimal experimental design in magnetic nanoparticle hyperthermia to address these challenges. By accounting for uncertainties in key parameters, such as tissue thermal conductivity, blood perfusion rate, and nanoparticle anisotropy constant, the framework maximizes mutual information between the observed data and model parameters, enhancing the accuracy of parameter estimation. A surrogate 1D model is employed to reduce computational complexity, allowing the identification of optimal magnetic field amplitudes across diverse initial conditions and scenarios, including single and multiple uncertain parameters. The results highlight the robustness of the optimization approach, demonstrating consistent convergence to a stable solution that is expected to enable precise temperature measurement and effective parameter recovery. This study underscores the potential for mutual information-based optimization to advance the planning of magnetic nanoparticle hyperthermia treatment and provides a foundation for future experimental and clinical applications. © 2025, The Authors. All rights reserved.

关键词： Hyperthermia therapy

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Solvers for Large-Scale Electronic Structure Theory: ELPA and ELSI

arXiv

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arXiv 2025年

作者： Karpov, Petr Marek, Andreas Melson, Tobias Pöppl, Alexander Yu, Victor Wen-Zhe Hourahine, Ben Garcia, Alberto Dawson, William Yao, Yi Huhn, William Moussa, Jonathan Hall, Sam Maurer, Reinhard Herath, Uthpala Lion, Konstantin Kokott, Sebastian Blum, Volker Max Planck Computing and Data Facility Garching Germany Intel Corporation Santa ClaraCA United States Thomas Lord Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Department of Physics SUPA University of Strathclyde John Anderson Building107 Rottenrow GlasgowG4 0NG United Kingdom Institut de Ciència de Materials de Barcelona ICMAB-CSIC Campus UAB Bellaterra08193 Spain RIKEN Center for Computational Science Kobe650-0047 Japan Molecular Simulations from First Principles e.V. BerlinD-14195 Germany Intel Corporation 500 Beaver Brook Road BoxboroughMA01719 United States Molecular Sciences Software Institute BlacksburgVA24060 United States University of Warwick United Kingdom Department of Chemistry Duke University DurhamNC27708 United States

In this contribution, we give an overview of the ELPA library and ELSI interface, which are crucial elements for large-scale electronic structure calculations in FHI-aims. ELPA is a key solver library that provides efficient solutions for both standard and generalized eigenproblems, which are central to the Kohn-Sham formalism in density functional theory (DFT). It supports CPU and GPU architectures, with full support for NVIDIA and AMD GPUs, and ongoing development for Intel GPUs. Here we also report the results of recent optimizations, leading to significant improvements in GPU performance for the generalized eigenproblem. ELSI is an open-source software interface layer that creates a well-defined connection between "user" electronic structure codes and "solver" libraries for the Kohn-Sham problem, abstracting the step between Hamilton and overlap matrices (as input to ELSI and the respective solvers) and eigenvalues and eigenvectors or density matrix solutions (as output to be passed back to the "user" electronic structure code). In addition to ELPA, ELSI supports solvers including LAPACK and MAGMA, the PEXSI and NTPoly libraries (which bypass an explicit eigenvalue solution), and several others. ELSI, ELPA, and other solver libraries supported in ELSI are mature, well-tested software, ensuring efficient support for large-scale simulations on current HPC systems. Future plans for ELPA include further optimization of GPU routines, particularly integrating the GPU collective communication libraries (NVIDIA’s NCCL and AMD’s RCCL) into the solution tridiagonal of the matrix problem and backtransformation of the eigenvectors in standard eigenproblem solvers. Similarly expanding support for Intel GPUs through deeper integration with Intel’s oneAPI libraries, specifically oneCCL, is planned. These advancements aim to ensure that ELPA, accessible either with its own Fortran/C/C++ APIs or via ELSI, remains at the forefront of large-scale computations, offering researchers

关键词： Open source software

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Model interpretability through the lens of computational complexity 20

Model interpretability through the lens of computational com...

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Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Pablo Barceló Mikaël Monet Jorge Pérez Bernardo Subercaseaux Institute for Mathematical and Computational Engineering PUC-Chile and Millennium Institute for Foundational Research on Data Chile Inria Lille France Department of Computer Science Universidad de Chile and Millennium Institute for Foundational Research on Data Chile

ISBN: (纸本)9781713829546

In spite of several claims stating that some models are more interpretable than others - e.g., "linear models are more interpretable than deep neural networks" - we still lack a principled notion of interpretability to formally compare among different classes of models. We make a step towards such a notion by studying whether folklore interpretability claims have a correlate in terms of computational complexity theory. We focus on local post-hoc explainability queries that, intuitively, attempt to answer why individual inputs are classified in a certain way by a given model. In a nutshell, we say that a class Ƈ1 of models is more interpretable than another class Ƈ2, if the computational complexity of answering post-hoc queries for models in Ƈ2 is higher than for those in Ƈ1. We prove that this notion provides a good theoretical counterpart to current beliefs on the interpretability of models; in particular, we show that under our definition and assuming standard complexity-theoretical assumptions (such as P ≠ NP), both linear and tree-based models are strictly more interpretable than neural networks. Our complexity analysis, however, does not provide a clear-cut difference between linear and tree-based models, as we obtain different results depending on the particular post-hoc explanations considered. Finally, by applying a finer complexity analysis based on parameterized complexity, we are able to prove a theoretical result suggesting that shallow neural networks are more interpretable than deeper ones.

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Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats (vol 16, 585, 2025)

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NATURE COMMUNICATIONS 2025年第1期16卷 1-15页

作者： Steinfeldt, Jakob Wild, Benjamin Buergel, Thore Pietzner, Maik Upmeier zu Belzen, Julius Vauvelle, Andre Hegselmann, Stefan Denaxas, Spiros Hemingway, Harry Langenberg, Claudia Landmesser, Ulf Deanfield, John Eils, Roland Department of Cardiology Angiology and Intensive Care Medicine Deutsches Herzzentrum der Charité (DHZC) Berlin Germany Charité – Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin Klinik/Centrum Berlin Germany Computational Medicine Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany Friede Springer Cardiovascular Prevention Center@Charite Charite - University Medicine Berlin Berlin Germany Institute of Cardiovascular Sciences University College London London UK Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany DZHK (German Centre for Cardiovascular Research) Partner Site Berlin Berlin Berlin Germany MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK Precision Health University Research Institute Queen Mary University of London and Barts NHS Trust London UK Center for Digital Health Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany Health Data Science Unit Heidelberg University Hospital and BioQuant Heidelberg Germany Institute of Health Informatics University College London London UK British Heart Foundation Data Science Centre London UK Health Data Research UK London UK National Institute for Health Research Biomedical Research Centre at University College London Hospitals London UK Institute for Medical Engineering and Science Massachusetts Institute of Technology Massachusetts USA Pattern Recognition and Image Analysis Lab University of Münster Münster Germany

The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1741 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,489 UK Biobank participants. Importantly, we observed discriminative improvements over basic demographic predictors for 1546 (88.8%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1115 (78.9%) of 1414 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.

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Identifying Reasons for Contraceptive Switching from Real-World data Using Large Language Models

arXiv

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arXiv 2024年

作者： Miao, Brenda Y. Williams, Christopher Y.K. Chinedu-Eneh, Ebenezer Zack, Travis Alsentzer, Emily Butte, Atul J. Chen, Irene Y. Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States Department of Medicine University of California San Francisco San FranciscoCA United States Helen Diller Family Comprehensive Cancer Center University of California San Francisco San FranciscoCA United States Division of General Internal Medicine Brigham and Women's Hospital BostonMA United States Harvard Medical School BostonMA United States Center for Data-driven Insights and Innovation University of California Office of the President OaklandCA United States Computational Precision Health University of California Berkeley University of California San Francisco BerkeleyCA United States Electrical Engineering and Computer Science University of California Berkeley BerkeleyCA United States Berkeley AI Research University of California Berkeley BerkeleyCA United States

Background: Understanding why patients switch contraceptives is of significant interest but these factors are often only captured in unstructured clinical notes and can be difficult to extract. We evaluate the zero-shot abilities of a recently developed large language model, GPT-4, to identify reasons for switching between classes of contraceptives from clinical notes. Methods: Clinical notes associated with contraceptive switches, defined as a difference in prescribed contraceptive modality between consecutive encounters, were selected from the UCSF Information Commons dataset. GPT-4 (HIPAA-compliant Microsoft Azure API) was used to extract contraceptive class started, stopped, and reasons for switching from these clinical notes. We evaluated six prompts on a subset of human annotated notes, and extracted reasons for contraceptive switching from the full dataset using the best prompt based on microF1 scores. BERTopic topic modeling was used to identify key reasons for contraceptive switching and enrichment of topics was assessed in patient subgroups stratified by race and ethnicity. Results: We extracted 1,964 contraceptive switches from 1,515 patients. Younger patients and patients with self-reported race/ethnicities listed as "Black/African American" and "Latinx" had significantly higher rates of switching. GPT-4 demonstrated extraction of contraceptives started and stopped had MicroF1 scores of 0.849 and 0.881, respectively. Accuracy of GPT-4 extracted reasons for switching was 91.4% and 2.2% contained hallucinations. Key reasons for switching included patient preference, adverse events, and insurance. "Weight gain/mood change" and "insurance coverage" topics were enriched in patients self-reporting as being "Black or African American", "Latinx", or "Multi-Race/Ethnicity". Conclusion: We demonstrate that GPT-4 can accurately extract reasons for contraceptive switching and identify certain reasons that are disproportionately found in specific demographic populati

关键词： Extraction

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Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation

arXiv

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arXiv 2023年

作者： Acosta, Fernando Torales Mikuni, Vinicius Nachman, Benjamin Arratia, Miguel Karki, Bishnu Milton, Ryan Karande, Piyush Angerami, Aaron Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States Department of Physics and Astronomy University of California RiversideCA92521 United States Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Nuclear and Chemical Science Division Lawrence Livermore National Laboratory LivermoreCA94550 United States

Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared. © 2023, CC BY.

关键词： Calorimeters

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A Modified Clustering Using Representatives to Enhance and Optimize Tracking and Monitoring of Maritime Traffic in Real-time Using Automatic Identification System data

A Modified Clustering Using Representatives to Enhance and O...

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International Conference on computational science and computational Intelligence (CSCI)

作者： Cheronika Manyfield-Donald Tor A. Kwembe Jing-Ru C. Cheng Computational Data-Enabled Science and Engineering Jackson State University Jackson MS USA Department of Mathematics and Statistical Sciences Jackson State University Jackson MS USA Information Technology Lab. Engineer Research and Development Center U.S. Army Corps of Engineers Vicksburg MS USA

ISBN: (纸本)9781665458429

In this paper, we introduce a modification of the Clustering Using Representatives (CURE) algorithm to enhance and optimize the tracking and monitoring of maritime traffic in real-time using the Automatic Identification System (AIS) data. In doing so, we present a 2-D points data collection system for the utilization of a modified unsupervised machine learning clustering method of the CURE algorithm integrated with a data streaming algorithm to develop a more efficient method that will directly assist in adverting accidents and support in vessels avoiding dangerous environments. Results are presented that show tracking in inland as well as open sea waterways.

关键词： Machine learning algorithms Tracking Scientific computing Clustering methods Clustering algorithms Machine learning data collection

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