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arXiv 2022年

作者： Karra, Satish Mehana, Mohamed Lubbers, Nicholas Chen, Yu Diaw, Abdourahmane Santos, Javier E. Pachalieva, Aleksandra Pavel, Robert S. Haack, Jeffrey R. McKerns, Michael Junghans, Christoph Kang, Qinjun Livescu, Daniel Germann, Timothy C. Viswanathan, Hari S. Computational Earth Science Group Earth and Environmental Sciences Division Los Alamos National Laboratory NM87545 United States Environmental Molecular Sciences Laboratory Pacific Northwest National Laboratory RichlandWA99354 United States Information Sciences Group Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los AlamosNM87545 United States Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzen518055 China RadiaSoft LLC 6525 Gunpark Dr. Suite 370-411 BoulderCO80301 United States Applied Computer Science Group Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los AlamosNM87545 United States Computational Physics and Methods Los Alamos National Laboratory Los AlamosNM87545 United States Physics and Chemistry of Materials Group Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States

Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements. For instance, quantitative predictions of transport in nanoporous media, critical to hydrocarbon extraction from tight shale formations, are impossible without accounting for molecular-level interactions. Similarly, inertial confinement fusion simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions. With these two disparate applications in mind, we develop a novel capability which uses an active learning approach to optimize the use of local fine-scale simulations for informing coarse-scale hydrodynamics. Our approach addresses three challenges: forecasting continuum coarse-scale trajectory to speculatively execute new fine-scale molecular dynamics calculations, dynamically updating coarse-scale from fine-scale calculations, and quantifying uncertainty in neural network models. © 2022, CC BY.

关键词： Molecular dynamics

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AN ADAPTIVE FINITE ELEMENT METHOD FOR TWO-DIMENSIONAL ELLIPTIC EQUATIONS WITH LINE DIRAC SOURCES

arXiv

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arXiv 2021年

作者： Cao, Huihui Li, Hengguang Yi, Nianyu Yin, Peimeng Hunan Key Laboratory for Computation and Simulation in Science and Engineering School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China Wayne State University Department of Mathematics DetroitMI48202 United States Multiscale Methods and Dynamics Group Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

In this paper, we propose a novel adaptive finite element method for an elliptic equation with line Dirac delta functions as a source term. We first study the well-posedness and global regularity of the solution in the whole domain. Instead of regularizing the singular source term and using the classical residual-based a posteriori error estimator, we propose a novel a posteriori estimator based on an equivalent transmission problem with zero source term and nonzero flux jumps on line fractures. The transmission problem is defined in the same domain as the original problem excluding on line fractures, and the solution is therefore shown to be more regular. The estimator relies on meshes conforming to the line fractures and its edge jump residual essentially uses the flux jumps of the transmission problem on line fractures. The error estimator is proven to be both reliable and efficient, an adaptive finite element algorithm is proposed based on the error estimator and the bisection refinement method. Numerical tests show that quasi-optimal convergence rates are achieved even for high order approximations and the adaptive meshes are only locally refined at singular points. © 2021, CC BY-NC-ND.

关键词： Fracture

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Quantum Inspired Optimization for Industrial Scale Problems

arXiv

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arXiv 2023年

作者： Banner, William P. Hadiashar, Shima Bab Mazur, Grzegorz Menke, Tim Ziolkowski, Marcin Kennedy, Ken Romero, Jhonathan Cao, Yudong Grover, Jeffrey A. Oliver, William D. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Zapata Computing Inc. 100 Federal St 20th Floor BostonMA02110 United States Department of Computational Methods in Chemistry Jagiellonian University Gronostajowa 2 Kraków30-387 Poland Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Harvard University CambridgeMA02138 United States BMW Group Information Technology Research Center 2 Research Dr. GreenvilleSC29607 United States

Model-based optimization, in concert with conventional black-box methods, can quickly solve large-scale combinatorial problems. Recently, quantum-inspired modeling schemes based on tensor networks have been developed which have the potential to better identify and represent correlations in datasets. Here, we use a quantum-inspired model-based optimization method TN-GEO to assess the efficacy of these quantum-inspired methods when applied to realistic problems. In this case, the problem of interest is the optimization of a realistic assembly line based on BMW's currently utilized manufacturing schedule. Through a comparison of optimization techniques, we found that quantum-inspired model-based optimization, when combined with conventional black-box methods, can find lower-cost solutions in certain contexts. Copyright © 2023, The Authors. All rights reserved.

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Particle In Cell Simulations of Mildly Relativistic Outflows in Kilonova Emissions

arXiv

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arXiv 2023年

作者： Rassel, Mohira Kilian, Patrick Aberham, Vito Spanier, Felix Lloyd-Ronning, Nicole Fryer, Chris L. Center for Theoretical Astrophysics Los Alamos National Laboratory Los AlamosNM87545 United States Center for Non Linear Studies Los Alamos National Laboratory Los AlamosNM87545 United States Space Science Institute 4765 Walnut St Suite B BoulderCO80301 United States Institut für Theoretische Astrophysik Universität Heidelberg Heidelberg69120 Germany Computational Physics and Methods Group Los Alamos National Laboratory Los AlamosNM87545 United States Department of Math Engineering & Science University of New Mexico Los AlamosNM87545 United States Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los AlamosNM87545 United States The University of Arizona TucsonAZ85721 United States Department of Physics and Astronomy The University of New Mexico AlbuquerqueNM87131 United States The George Washington University WashingtonDC20052 United States

The electromagnetic emission from neutron star mergers is comprised of multiple components. Synchrotron emission from the disk-powered jet as well as thermal emission from the merger ejecta (powered by a variety of sources) are among the most studied sources. The low masses and high velocities of the merger ejecta quickly develop conditions where emission from collisionless shocks becomes critical and synchrotron emission from the merger ejecta constitutes a third component to the observed signal. The aim of this project is to examine shock development, magnetic field generation and particle acceleration in the case of mildly relativistic shocks, which are expected when the tidal ejecta of neutron star mergers drive a shock into the external medium. Using LANL’s VPIC (vector particle-in-cell) code, we have run simulations of such mildly-relativistic, collisionless, weakly-magnetized plasmas and compute the resultant magnetic fields and particle energy spectra. We show the effects of varying plasma conditions, as well as explore the validity of using different proton to electron mass ratios in VPIC. Our results have implications for observing late-time electromagnetic counterparts to gravitational wave detections of neutron star mergers. Copyright © 2023, The Authors. All rights reserved.

关键词： Merging

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Parallel algorithms for tensor train arithmetic

arXiv

引用

arXiv 2020年

作者： Al Daas, Hussam Ballard, Grey Benner, Peter Department of Computational Methods in Systems and Control Theory Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Computer Science Department Wake Forest University Winston SalemNC United States

We present efficient and scalable parallel algorithms for performing mathematical operations for low-rank tensors represented in the tensor train (TT) format. We consider algorithms for addition, elementwise multiplication, computing norms and inner products, orthonormalization, and rounding (rank truncation). These are the kernel operations for applications such as iterative Krylov solvers that exploit the TT structure. The parallel algorithms are designed for distributed- memory computation, and we propose a data distribution and strategy that parallelizes computations for individual cores within the TT format. We analyze the computation and communication costs of the proposed algorithms to show their scalability, and we present numerical experiments that demonstrate their efficiency on both shared-memory and distributed-memory parallel systems. For example, we observe better single-core performance than the existing MATLAB TT-Toolbox in rounding a 2GB TT tensor, and our implementation achieves a 34× speedup using all 40 cores of a single node. We also show nearly linear parallel scaling on larger TT tensors up to over 10,000 cores for all mathematical operations. Copyright © 2020, The Authors. All rights reserved.

关键词： Tensors

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ESCAPED: Efficient secure and private dot product framework for kernel-based machine learning algorithms with applications in healthcare

arXiv

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arXiv 2020年

作者： Ünal, Ali Burak Akgün, Mete Pfeifer, Nico Methods in Medical Informatics Department of Computer Science University of Tuebingen Germany Translational Bioinformatics University Hospital Tuebingen Tuebingen Germany Statistical Learning in Computational Biology Max Planck Institute for Informatics Saarbrücken Germany

To train sophisticated machine learning models one usually needs many training samples. Especially in healthcare settings these samples can be very expensive, meaning that one institution alone usually does not have enough on its own. Merging privacy-sensitive data from different sources is usually restricted by data security and data protection measures. This can lead to approaches that reduce data quality by putting noise onto the variables (e.g., in ϵ differential privacy) or omitting certain values (e.g., for k-anonymity). Other measures based on cryptographic methods can lead to very time-consuming computations, which is especially problematic for larger multi-omics data. We address this problem by introducing ESCAPED, which stands for Efficient SeCure And PrivatE Dot product framework, enabling the computation of the dot product of vectors from multiple sources on a third-party, which later trains kernel-based machine learning algorithms, while neither sacrificing privacy nor adding noise. We evaluated our framework on drug resistance prediction for HIV-infected people and multi-omics dimensionality reduction and clustering problems in precision medicine. In terms of execution time, our framework significantly outperforms the best-fitting existing approaches without sacrificing the performance of the algorithm. Even though we only show the benefit for kernel-based algorithms, our framework can open up new research opportunities for further machine learning models that require the dot product of vectors from multiple sources. © 2020, CC BY.

关键词： Health care

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Realistic Kilonova Up Close

arXiv

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arXiv 2022年

作者： Stewart, Alexandra Ruth Lo, Li-Ta Korobkin, Oleg Sagert, Irina Loiseau, Julien Lim, Hyun Kaltenborn, Mark Alexander Mauney, Christopher Michael Miller, Jonah Maxwell CCS-2 Computational Physics and Methods Los Alamos National Laboratories Los AlamosNM87545 United States CCS-3 Information Sciences Los Alamos National Laboratories Los AlamosNM87545 United States CCS-7 Applied Computer Science Los Alamos National Laboratories Los AlamosNM87545 United States HPC Environments Los Alamos National Laboratories Los AlamosNM87545 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics The George Washington University WashingtonDC20052 United States

Neutron star mergers are cosmic catastrophes that produce some of the most energetic observed phenomena: short gamma-ray bursts, gravitational wave signals, and kilonovae. The latter are optical transients, powered by radioactive nuclides which are synthesized when the neutron-rich ejecta of a disrupted neutron star undergoes decompression. We model this decompression phase using data from simulations of post-merger accretion disk winds. We use smoothed particle hydrodynamics with realistic nuclear heating to model the expansion over multiple scales, from initially several thousand km to billions of km. We then render a realistic image of a kilonova ejecta as it would appear for a nearby observer. This is the first time such a visualization is performed using input from state-of-the-art accretion disk simulations, nuclear physics and atomic physics. The volume rendering in our model computes an opacity transfer function on the basis of the physical opacity, varying significantly with the inhomogeneity of the neutron richness in the ejecta. Other physical quantities such as temperature or electron fraction can be visualized using an independent color transfer function. We discuss several difficulties with the ParaView application that we encountered during the visualization process, and give descriptions of our solutions and workarounds which could be used for future improvements. © 2022, CC BY.

关键词： Merging

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A New Constraint on the Nuclear Equation of State from Statistical Distributions of Compact Remnants of Supernovae

arXiv

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arXiv 2021年

作者： Meskhi, Mikhail M. Wolfe, Noah E. Dai, Zhenyu Fröhlich, Carla Miller, Jonah M. Wong, Raymond K.W. Vilalta, Ricardo Department of Computer Science University of Houston HoustonTX77204-3010 United States Department of Physics North Carolina State University RaleighNC27695 United States CCS-2 Computational Physics and Methods Los Alamos National Laboratory Los AlamosNM87544 United States Center for Theoretical Astrophysics Los Alamos National Laandoratory Los AlamosNM87544 United States Department of Statistics Texas A&M University College StationTX77843 United States

Understanding how matter behaves at the highest densities and temperatures is a major open problem in both nuclear physics and relativistic astrophysics. Our understanding of such behavior is often encapsulated in the so-called high-temperature nuclear equation of state, which influences compact binary mergers, core-collapse supernovae, and other phenomena. Our focus is on the type (either black hole or neutron star) and mass of the remnant of the core collapse of a massive star. For each of six candidate equations of state, we use a very large suite of spherically symmetric supernova models to generate a sample of synthetic populations of such remnants. We then compare these synthetic populations to the observed remnant population. Our study provides a novel constraint on the high-temperature nuclear equation of state and describes which EOS candidates are more or less favored by an information-theoretic metric. Copyright © 2021, The Authors. All rights reserved.

关键词： Supernovae

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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A DG-IMEX method for two-moment neutrino transport: Nonlinear solvers for neutrino-matter coupling

arXiv

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arXiv 2021年

作者： Laiu, M. Paul Endeve, Eirik Chu, Ran Harris, J. Austin Messer, O.E. Bronson Multiscale Methods Group Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Physics and Astronomy University of Tennessee Knoxville KnoxvilleTN37996 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Physics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

Neutrino-matter interactions play an important role in core-collapse supernova (CCSN) explosions as they contribute to both lepton number and/or four-momentum exchange between neutrinos and matter, and thus act as the agent for neutrino-driven explosions. Due to the multiscale nature of neutrino transport in CCSN simulations, an implicit treatment of neutrino-matter interactions is desired, which requires solutions of coupled nonlinear systems in each step of the time integration scheme. In this paper we design and compare nonlinear iterative solvers for implicit systems with energy coupling neutrino-matter interactions commonly used in CCSN simulations. Specifically, we consider electron neutrinos and antineutrinos, which interact with static matter configurations through the Bruenn 85 opacity set. The implicit systems arise from the discretization of a non-relativistic two-moment model for neutrino transport, which employs the discontinuous Galerkin (DG) method for phase-space discretization and an implicit-explicit (IMEX) time integration scheme. In the context of this DG-IMEX scheme, we propose two approaches to formulate the nonlinear systems - a coupled approach and a nested approach. For each approach, the resulting systems are solved with Anderson-accelerated fixed-point iteration and Newton's method. The performance of these four iterative solvers has been compared on relaxation problems with various degree of collisionality, as well as proto-neutron star deleptonization problems with several matter profiles adopted from spherically symmetric CCSN simulations. Numerical results suggest that the nested Anderson-accelerated fixed-point solver is more efficient than other tested solvers for solving implicit nonlinear systems with energy coupling neutrino-matter interactions. Copyright © 2021, The Authors. All rights reserved.

关键词： Galerkin methods

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