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arXiv 2022年

作者： Pachev, Benjamin Arora, Prateek del-Castillo-Negrete, Carlos Valseth, Eirik Dawson, Clint Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX78712 United States Department of Civil and Urban Engineering New York University BrooklynNY11201 United States The Department of Data Science Norwegian University of Life Sciences Elizabeth Stephansens vei 15 Ås1430 Norway Simula Research Laboratory Kristian Augusts gate 23 Oslo0164 Norway

Storm surge is a major natural hazard in coastal regions, responsible both for significant property damage and loss of life. Accurate, efficient models of storm surge are needed both to assess long-term risk and to guide emergency management decisions. While high-fidelity regional- and global-ocean circulation models such as the ADvanced CIRCulation (ADCIRC) model can accurately predict storm surge, they are very computationally expensive. Consequently, there have been a number of efforts in recent years to develop data-driven surrogate models for storm surge. Here we develop a novel surrogate model for peak storm surge prediction based on a multi-stage approach. In the first stage, points are classified as inundated or not. In the second, the level of inundation is predicted for each point. Additionally, we propose a new formulation of the surrogate problem in which storm surge is predicted independently for each point. This formulation allows for predictions to be made directly for locations not present in the training data, and significantly reduces the number of required model parameters. We demonstrate our modeling framework on two study areas: the Texas coast and the northern portion of the Alaskan coast. For Texas, the model is trained with a database of 446 synthetic hurricanes. The model is able to accurately match ADCIRC predictions on a test set of synthetic storms. We further present a test of the model on Hurricanes Ike (2008) and Harvey (2017). For both storms, we find that the model predictions have comparable accuracy to ADCIRC hindcasts when compared to actual observational data. For Alaska, the model is trained on a dataset of 109 historical surge events. We test the surrogate model on actual surge events including the recent Typhoon Merbok (2022) that take place after the events in the training data. As with the Texas dataset, the surrogate model achieves similar performance to ADCIRC on real events when compared to observational data. In both cas

关键词： Machine learning

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Quantum mechanical dataset of 836k neutral closed shell molecules with upto 5 heavy atoms from CNOFSiPSClBr

arXiv

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arXiv 2024年

作者： Khan, Danish Benali, Anouar Kim, Scott Y.H. von Rudorff, Guido Falk von Lilienfeld, O. Anatole Chemical Physics Theory Group Department of Chemistry University of Toronto St. George Campus TorontoON Canada Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Computational Science Division Argonne National Laboratory ArgonneIL60439 United States Institute of Chemistry University of Kassel Kassel34109 Germany Kassel34132 Germany Department of Materials Science and Engineering University of Toronto St. George Campus TorontoON Canada ML Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Department of Physics University of Toronto St. George Campus TorontoON Canada Acceleration Consortium University of Toronto TorontoON Canada

We introduce the Vector-QM24 (VQM24) dataset which comprehensively covers all possible neutral closed shell small organic and inorganic molecules and their conformers that contain up to five heavy atoms (non-hydrogen) consisting of p-block elements C, N, O, F, Si, P, S, Cl, Br. This dataset has been systematically generated by considering all combinatorially possible stoichiometries, and graphs (according to Lewis rules), along with all stable conformers identified by GFN2-xTB. We have used density functional theory (ωB97X-D3/cc-pVDZ) to optimize the geometries of 577k conformational isomers corresponding to 258k constitutional isomers consistent with 5,599 unique stoichiometries. Single point diffusion quantum Monte Carlo (DMC@PBE0(ccECP/cc-pVQZ)) energies are reported for the sub-set of all the energetically lowest conformers (10,793 molecules) with up to 4 heavy atoms. Apart from graphs, geometries, rotational constants, and vibrational normal modes, VQM24 includes internal, atomization, electron-electron repulsion, exchange-correlation, dispersion, vibrational frequency, Gibbs free, enthalpy, ZPV and molecular orbital energies;as well as entropy, and heat capacities. Electronic properties include multipole moments (dipole, quadrupole, octupole, hexadecapole), electrostatic potentials at nuclei (aka"alchemical potential"), Mulliken charges, and Kohn-Sham orbitals. Supervised machine learning (ML) models of atomization energies on the energetically lowest conformers for the 258k constitutional isomers indicate a significantly more challenging benchmark than the previously introduced QM9 dataset with none of our models reaching chemical accuracy. VQM24 represents an accurate and unbiased benchmark dataset ideal for assessing the efficiency, accuracy and transferability of quantum ML models of real systems. © 2024, CC BY.

关键词： Isomers

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Tensor Denoising via Amplification and Stable Rank Methods

arXiv

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arXiv 2023年

作者： Gryak, Jonathan Najarian, Kayvan Derksen, Harm Department of Computer Science Queens College City University of New York New YorkNY United States Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States Department of Emergency Medicine University of Michigan Ann ArborMI United States Electrical and Computer Engineering College of Engineering University of Michigan Ann ArborMI United States Michigan Institute for Data Science University of Michigan Ann ArborMI United States Max Harry Weil Institute for Critical Care Research and Innovation University of Michigan Ann ArborMI United States Department of Mathematics Northeastern University BostonMA United States

Tensors in the form of multilinear arrays are ubiquitous in data science applications. Captured real-world data, including video, hyperspectral images, and discretized physical systems, naturally occur as tensors and often come with attendant noise. Under the additive noise model and with the assumption that the underlying clean tensor has low rank, many denoising methods have been created that utilize tensor decomposition to effect denoising through low rank tensor approximation. However, all such decomposition methods require estimating the tensor rank, or related measures such as the tensor spectral and nuclear norms, all of which are NP-hard problems. In this work we leverage our previously developed framework of tensor amplification, which provides good approximations of the spectral and nuclear tensor norms, to denoising synthetic tensors of various sizes, ranks, and noise levels, along with real-world tensors derived from physiological signals. We also introduce two new notions of tensor rank – stable slice rank and stable X-rank – and new denoising methods based on their estimation. The experimental results show that in the low rank context, tensor-based amplification provides comparable denoising performance in high signal-to-noise ratio (SNR) settings and superior performance in noisy (i.e., low SNR) settings, while the stable X-rank method achieves superior denoising performance on the physiological signal data. Copyright © 2023, The Authors. All rights reserved.

关键词： Amplification

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Higher-order rich-club phenomenon in collaborative research grant networks

arXiv

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arXiv 2022年

作者： Nakajima, Kazuki Shudo, Kazuyuki Masuda, Naoki Department of Mathematical and Computing Science Tokyo Institute of Technology Meguro-ku Tokyo152-8552 Japan Department of Mathematics State University of New York at Buffalo Buffalo14260 United States Academic Center for Computing and Media Studies Kyoto University Sakyo-ku Kyoto606-8501 Japan Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo14260 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

Modern scientific work, including writing papers and submitting research grant proposals, increasingly involves researchers from different institutions. In grant collaborations, it is known that institutions involved in many collaborations tend to densely collaborate with each other, forming rich clubs. Here we investigate higher-order rich-club phenomena in networks of collaborative research grants among institutions and their associations with research impact. Using publicly available data from the National science Foundation in the US, we construct a bipartite network of institutions and collaborative grants, which distinguishes among the collaboration with different numbers of institutions. By extending the concept and algorithms of the rich club for dyadic networks to the case of bipartite networks, we find rich clubs both in the entire bipartite network and the bipartite subnetwork induced by the collaborative grants involving a given number of institutions up to five. We also find that the collaborative grants within rich clubs tend to be more productive in a per-dollar sense than the control. Our results highlight advantages of collaborative grants among the institutions in the rich clubs. Copyright © 2022, The Authors. All rights reserved.

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Cosmological phase-space analysis of f(G)-theories of gravity

arXiv

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arXiv 2025年

作者： Papagiannopoulos, Giannis Luongo, Orlando Leon, Genly Paliathanasis, Andronikos Department of Physics National & Kapodistrian University of Athens Zografou Campus GR 157 73 Athens Greece School of Science and Technology University of Camerino Via Madonna delle Carceri Camerino62032 Italy Department of Nanoscale Science and Engineering University at Albany-SUNY AlbanyNY12222 United States INAF - Osservatorio Astronomico di Brera Milano Italy Sezione di Perugia Perugia06123 Italy Al-Farabi Kazakh National University Al-Farabi av. 71 Almaty050040 Kazakhstan Departamento de Matemáticas Universidad Católica del Norte Avda. Angamos 0610 Casilla Antofagasta1280 Chile Institute of Systems Science Department of Mathematics Faculty of Applied Sciences Durban University of Technology Durban4000 South Africa School for Data Science and Computational Thinking Department of Mathematical Sciences Stellenbosch University Stellenbosch7602 South Africa South Africa

The impact of topological terms that modify the Hilbert-Einstein action is here explored by virtue of a further f(G) contribution. In particular, we investigate the phase-space stability and critical points of an equivalent scalar field representation that makes use of a massive field, whose potential is function of the topological correction. To do so, we introduce to the gravitational Action Integral a Lagrange multiplier and model the modified Friedmann equations by virtue of new non-dimensional variables. We single out dimensionless variables that permit a priori the Hubble rate change of sign, enabling regions in which the Hubble parameter either vanishes or becomes negative. In this respect, we thus analyze the various possibilities associated with a universe characterized by such topological contributions and find the attractors, saddle points and regions of stability, in general. The overall analysis is carried out considering the exponential potential first and then shifting to more complicated cases, where the underlying variables do not simplify. We compute the eigenvalues of our coupled differential equations and, accordingly, the stability of the system, in both a spatially-flat and non-flat universe. Quite remarkably, regardless of the spatial curvature, we show that a stable de Sitter-like phase that can model current time appears only a small fraction of the entire phase-space, suggesting that the model under exam is unlikely in describing the whole universe dynamics, i.e., the topological terms appear disfavored in framing the entire evolution of the universe. © 2025, CC BY.

关键词： Topology

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Vacancy-Type Defects in HfO2 Layers and Their Role in Amorphous-to-Crystalline Transition Studied by Monoenergetic Positron Beams

引用

IEEE Transactions on Semiconductor Manufacturing 2025年

作者： Uedono, Akira Kawakami, Kazuya Kuribara, Tatsunori Hasunuma, Ryu Harashima, Yosuke Shigeta, Yasuteru Ni, Zeyuan Yan, Ruolin Nakamura, Hideo Notake, Akira Moriya, Tsuyoshi Michishio, Koji Ishibashi, Shoji University of Tsukuba Faculty of Pure and Applied Science Tsukuba Ibaraki305-8573 Japan Nara Institute of Science and Technology Graduate School of Science and Technology Ikoma Nara630-0192 Japan Tokyo Electron Technology Solutions Ltd S-Technology Development Center Nirasaki Yamanashi650 Japan Tokyo Electron Technology Solutions Ltd Development Department1 Nirasaki Yamanashi407-0192 Japan Tokyo Electron Ltd Digital Design Center Sapporo Hokkaido060-0003 Japan Tokyo Electron Technology Solutions Ltd Software and Data Engineering Division Nirasaki Yamanashi407-0192 Japan National Institute of Advanced Industrial Science and Technology Research Institute for Measurement and Analytical Instrumentation Tsukuba Ibaraki305-8568 Japan National Institute of Advanced Industrial Science and Technology Research Center for Computational Design of Advanced Functional Materials Tsukuba Ibaraki305-8568 Japan

The annealing properties of vacancies in HfO2 layers deposited on Si substrates and their role in amorphous-to-crystalline transition were studied with monoenergetic positron beams. HfO2 layers with a thickness of 4.30 nm were fabricated by the atomic layer deposition technique. The major vacancytype defects in these layers were identified as a Hf vacancy (VHf) coupled with multiple oxygen vacancies (VOs) and larger vacancy clusters. After annealing at 500°C, the concentration of vacancy clusters started to increase, which was attributed to the agglomeration of intrinsic open spaces in the amorphous phase upon the phase transition from amorphous to monoclinic crystalline phases. The amorphous-crystalline transition started near the interface between the HfO2 layer and bottom electrodes (TiN). After the crystallization, the concentration of vacancy clusters decreased as the annealing temperature increased. © 2025 IEEE.

关键词： Hafnium oxides

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Numerical investigation of abdominal aortic aneurysm hemodynamics using the reduced unified continuum formulation for vascular fluid-structure interaction

arXiv

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arXiv 2022年

作者： Lan, Ingrid S. Liu, Ju Yang, Weiguang Marsden, Alison L. Department of Bioengineering Stanford University Stanford CA94305 United States Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China Guangdong-Hong Kong-Macao Jt. Lab. for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and Technology Guangdong Shenzhen518055 China Stanford University Stanford CA94305 United States Institute for Computational and Mathematical Engineering Stanford University Stanford CA94305 United States

We recently demonstrated the reduction of the unified continuum and variational multiscale formulation to a computationally efficient fluid-structure interaction (FSI) formulation via three sound modeling assumptions pertaining to the vascular wall. Similar to the coupled momentum method introduced by Figueroa et al., the resulting semi-discrete formulation yields a monolithically coupled FSI system posed in an Eulerian frame of reference with only a minor modification of the fluid boundary integral. To achieve uniform second-order temporal accuracy and user-controlled high-frequency algorithmic damping, we adopt the generalized-α method for uniform temporal discretization of the entire coupled system. In conjunction with a fully consistent, segregated predictor multi-corrector algorithm preserving the block structure of the incompressible Navier-Stokes equations in the implicit solver's associated linear system, a three-level nested block preconditioner is adopted for improved representation of the Schur complement. In this work, we apply our reduced unified continuum formulation to an appropriately prestressed patient-specific abdominal aortic aneurysm and investigate the effects of varying spatial distributions of wall properties on hemodynamic and vascular wall quantities of interest. © 2022, CC BY-NC-ND.

关键词： Continuum mechanics

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A Local Polyak-Lojasiewicz and Descent Lemma of Gradient Descent For Overparametrized Linear Models

arXiv

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arXiv 2025年

作者： Xu, Ziqing Min, Hancheng Tarmoun, Salma Mallada, Enrique Vidal, René Department of Statistics and Data Science the Wharton School University of Pennsylvania United States University of Pennsylvania United States Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania United States Department of Electrical and Computer Engineering Johns Hopkins University United States Department of Electrical and Systems Engineering Department of Radiology University of Pennsylvania United States

Most prior work on the convergence of gradient descent (GD) for overparameterized neural networks relies on strong assumptions on the step size (infinitesimal), the hidden-layer width (infinite), or the initialization (large, spectral, balanced). Recent efforts to relax these assumptions focus on two-layer linear networks trained with the squared loss. In this work, we derive a linear convergence rate for training two-layer linear neural networks with GD for general losses and under relaxed assumptions on the step size, width, and initialization. A key challenge in deriving this result is that classical ingredients for deriving convergence rates for nonconvex problems, such as the Polyak-Lojasiewicz (PL) condition and Descent Lemma, do not hold globally for overparameterized neural networks. Here, we prove that these two conditions hold locally with local constants that depend on the weights. Then, we provide bounds on these local constants, which depend on the initialization of the weights, the current loss, and the global PL and smoothness constants of the non-overparameterized model. Based on these bounds, we derive a linear convergence rate for GD. Our convergence analysis not only improves upon prior results but also suggests a better choice for the step size, as verified through our numerical experiments. © 2025, CC BY.

关键词： Dynamic models

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Spatiotemporal Image Reconstruction to Enable High-Frame Rate Dynamic Photoacoustic Tomography with Rotating-Gantry Volumetric Imagers

arXiv

引用

arXiv 2023年

作者： Cam, Refik Mert Wang, Chao Thompson, Weylan Ermilov, Sergey A. Anastasio, Mark A. Villa, Umberto University of Illinois Urbana-Champaign Department of Electrical & Computer Engineering 306 North Wright Street UrbanaIL61801 United States Department of Statistics and Data Science National University of Singapore Singapore PhotoSound Technologies Inc. HoustonTX77036 United States University of Illinois Urbana-Champaign Department of Bioengineering 1406 West Green Street UrbanaIL61801 United States The University of Texas at Austin Oden Institute for Computational Engineering & Sciences 201 East 24th Street AustinTX78712 United States

Significance: Dynamic photoacoustic computed tomography (PACT) is a valuable imaging technique for monitoring physiological processes. However, current dynamic PACT imaging techniques are often limited to two-dimensional (2D) spatial imaging. While volumetric PACT imagers are commercially available, these systems typically employ a rotating measurement gantry in which the tomographic data are sequentially acquired, as opposed to being acquired simultaneously at all views. Because the dynamic object varies during the data-acquisition process, the sequential data-acquisition process poses substantial challenges to image reconstruction associated with data incompleteness. The proposed image reconstruction method is highly significant in that it will address these challenges and enable volumetric dynamic PACT imaging with existing preclinical imagers. Aim: The aim of this study is to develop a spatiotemporal image reconstruction method for dynamic PACT that can be applied to commercially available volumetric PACT imagers that employ a sequential scanning strategy. The proposed reconstruction method aims to overcome the challenges caused by the limited number of tomographic measurements acquired per frame. Approach: A low-rank matrix estimation-based spatiotemporal image reconstruction method (LRME-STIR) is proposed to enable dynamic volumetric PACT. The LRME-STIR method leverages the spatiotemporal redundancies in the dynamic object to accurately reconstruct a four-dimensional (4D) spatiotemporal image. Results: The conducted numerical studies substantiate the LRME-STIR method’s efficacy in reconstructing 4D dynamic images from tomographic measurements acquired with a rotating measurement gantry. The experimental study demonstrates the method’s ability to faithfully recover the flow of a contrast agent at a frame rate of 0.1 seconds, even when only a single tomographic measurement per frame is available. Conclusions: The proposed LRME-STIR method offers a promising solu

关键词： Image reconstruction

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Cluster expansion by transfer learning for phase stability predictions

arXiv

引用

arXiv 2023年

作者： Dana, A. Mu, L. Gelin, S. Sinnott, S.B. Dabo, I. Department of Materials Science and Engineering The Pennsylvania State University University ParkPA16802 United States Materials Research Institute The Pennsylvania State University University ParkPA16802 United States Department of Chemistry The Pennsylvania State University University ParkPA16802 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Department of Physics The Pennsylvania State University University ParkPA16802 United States

Recent progress towards universal machine-learned interatomic potentials holds considerable promise for materials discovery. Yet the accuracy of these potentials for predicting phase stability may still be limited. In contrast, cluster expansions provide accurate phase stability predictions but are computationally demanding to parameterize from first principles, especially for structures of low dimension or with a large number of components, such as interfaces or multimetal catalysts. We overcome this trade-off via transfer learning. Using Bayesian inference, we incorporate prior statistical knowledge from machine-learned and physics-based potentials, enabling us to sample the most informative configurations and to efficiently fit first-principles cluster expansions. This algorithm is tested on Pt:Ni, showing robust convergence of the mixing energies as a function of sample size with reduced statistical fluctuations. Copyright © 2023, The Authors. All rights reserved.

关键词： Density functional theory

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