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Thermal disorder and phonon softening in the ferroelectric phase transition of lead titanate

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Physical Review B 2025年第9期111卷 094113-094113页

作者： Pinchen Xie Yixiao Chen Weinan E Roberto Car Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA AI for Science Institute Beijing China and Center for Machine Learning Research and School of Mathematical Sciences Peking University Beijing China

We report a molecular dynamics study of ab initio quality of the ferroelectric phase transition in crystalline PbTiO3. We model anharmonicity accurately in terms of potential energy and polarization surfaces trained on density functional theory data with modern machine learning techniques. Our simulations demonstrate that the transition has a strong order-disorder character, in agreement with diffraction experiments, and provide fresh insight into the approach to equilibrium across the phase transition. We find that the emergence and disappearance of the macroscopic polarization is driven by dipolar switching at the nanometer scale. We also computed the infrared optical absorption spectra in both the ferroelectric and the paraelectric phases, finding good agreement with the experimental Raman frequencies. Often, the almost ideal displacive character of the soft mode detected by Raman scattering in the paraelectric phase has been contrasted with the order-disorder character of the transition suggested by diffraction experiments. We settle this issue by showing that the soft mode coexists with a strong Debye relaxation associated with thermal disordering of the dipoles. The Debye relaxation feature is centered at zero frequency and appears near the transition temperature in both the ferroelectric and the paraelectric phases.

关键词： Ferroelectricity

An artificial viscosity approach to high order entropy stable discontinuous Galerkin methods

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arXiv 2025年

作者： Chan, Jesse Department of Computational Applied Mathematics and Operations Research Rice University 6100 Main St HoustonTX77005 United States

Entropy stable discontinuous Galerkin (DG) methods improve the robustness of high order DG simulations of nonlinear conservation laws. These methods yield a semi-discrete entropy inequality, and rely on an algebraic flux differencing formulation which involves both summation-by-parts (SBP) discretization matrices and entropy conservative two-point finite volume fluxes. However, explicit expressions for such two-point finite volume fluxes may not be available for all systems, or may be computationally expensive to compute. This paper proposes an alternative approach to constructing entropy stable DG methods using an artificial viscosity coefficient based on the local violation of a cell entropy inequality and the local entropy dissipation. The resulting method recovers the same global semi-discrete entropy inequality that is satisfied by entropy stable flux differencing DG methods. The artificial viscosity coefficients are parameter-free and locally computable over each cell, and the resulting artificial viscosity preserves both high order accuracy and a hyperbolic maximum stable time-step size under explicit time-stepping. Copyright © 2025, The Authors. All rights reserved.

关键词： Viscosity

Global Bounds for the Error in Solutions of Linear Hyperbolic Systems due to Inaccurate Boundary Geometry

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arXiv 2025年

作者： Kopriva, David A. Winters, Andrew R. Nordström, Jan Department of Mathematics The Florida State University TallahasseeFL32306 United States Computational Science Research Center San Diego State University San DiegoCA United States Department of Mathematics Applied Mathematics Linköping University Linköping581 83 Sweden Department of Mathematics and Applied Mathematics University of Johannesburg P.O. Box 524 Auckland Park2006 South Africa

We derive global estimates for the error in solutions of linear hyperbolic systems due to inaccurate boundary geometry. We show that the error is bounded by data and bounded in time when the solutions in the true and approximate domains are bounded. We show that boundary data evaluation errors due to the incorrect locations of the boundaries are secondary effects, whereas the primary errors are from the Jacobian and metric terms. In two space dimensions, specifically, we show that to lowest order the errors are proportional to the errors in the boundary curves and their derivatives. The results illustrate the importance of accurately approximating boundaries, and they should be helpful for high-order mesh generation and the design of optimization algorithms for boundary approximations. Copyright © 2025, The Authors. All rights reserved.

关键词： Mesh generation

P-ORDER: A UNIFIED CONVERGENCE-ANALYSIS FRAMEWORK FOR MULTIVARIATE ITERATIVE METHODS

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arXiv 2025年

作者： Jiao, Xiangmin Gao, Hongji Department of Applied Mathematics & Statistics Institute for Advanced Computational Science Stony Brook University Stony BrookNY11794 United States Department of Applied Mathematics & Statistics Stony Brook University Stony BrookNY11794 United States

We propose P-order (Power-order), a unified, norm-independent framework for quantifying the convergence rates of iterative methods. Standard analyses based on Q-order are norm-dependent and require some uniformity of error reductions asymptotically. Although the Ortega–Rheinboldt R-order supports non-uniform (including non-monotonic sequences) and is norm-independent, it does not differentiate among various sublinear rates and has ambiguities for some superlinear rates. In contrast, our proposed framework parameterizes the convergence rate in direct analogy with asymptotic notation in combinatorial algorithm analysis (including little-o, big-Θ, and little-ω), thereby precisely distinguishing linear, sublinear (e.g., fractional-power), and superlinear (e.g., linearithmic) regimes. We also introduce two subclasses of P-order: Quasi-Uniform P-order (QUP) and Uniform P-order (UP), and show their relations with Q-order and R-order. We demonstrate the ease-of-use and flexibility of QUP-order by analyzing fixed-point iterations and show that P-order can provide tighter bounds on the convergence rate than R-order for both sublinearly and superlinearly convergent cases while avoiding some ambiguities in R-order. Furthermore, we present a refined analysis of Newton’s method for nonlinear equations with a wide spectrum of sublinear and superlinear convergence rates (including a newly defined anit-liearithmic rate). Copyright © 2025, The Authors. All rights reserved.

关键词： Combinatorial optimization

Cheaper and more noise-resilient quantum state preparation using eigenvector continuation

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Physical Review A 2025年第3期111卷 032607-032607页

作者： Anjali A. Agrawal João C. Getelina Akhil Francis A. F. Kemper Department of Physics North Carolina State University Raleigh North Carolina 27695 USA Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA

Subspace methods are powerful, noise-resilient methods that can effectively prepare ground states on quantum computers. The challenge is to get a subspace with a small condition number that spans the states of interest using minimal quantum resources. In this work, we will use eigenvector continuation to build a subspace from the low-lying states of a set of Hamiltonians. The basis vectors are prepared using truncated versions of standard state preparation methods such as imaginary-time evolution (ITE), adiabatic state preparation (ASP), and variational quantum eigensolver. By using these truncated methods combined with eigenvector continuation, we can directly improve upon them, obtaining more accurate ground-state energies at a reduced cost. We use several spin systems to demonstrate convergence even when methods like ITE and ASP fail, such as ASP in the presence of level crossings and ITE with vanishing energy gaps. We also showcase the noise resilience of this approach beyond the gains already made by having shallower quantum circuits. Our findings suggest that eigenvector continuation can be used to improve existing state preparation methods in the near term.

关键词： Quantum computers

CLEARING SECTIONS OF LATTICE LIABILITY NETWORKS

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arXiv 2025年

作者： Ghrist, Robert Gould, Julian Lopez, Miguel Riess, Hans Department of Mathematics and Electrical & Systems Engineering University of Pennsylvania Philadelphia PA Department of Mathematics University of Pennsylvania Philadelphia PA Applied Mathematics & Computational Science University of Pennsylvania Philadelphia PA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA

Modern financial networks involve complex obligations that transcend simple monetary debts: multiple currencies, prioritized claims, supply chain dependencies, and more. We present a mathematical framework that unifies and extends these scenarios by recasting the classical Eisenberg-Noe model of financial clearing in terms of lattice liability networks. Each node in the network carries a complete lattice of possible states, while edges encode nominal liabilities. Our framework generalizes the scalar-valued clearing vectors of the classical model to lattice-valued clearing sections, preserving the elegant fixed-point structure while dramatically expanding its descriptive power. Our main theorem establishes that such networks possess clearing sections that themselves form a complete lattice under the product order. This structure theorem enables tractable analysis of equilibria in diverse domains, including multi-currency financial systems, decentralized finance with automated market makers, supply chains with resource transformation, and permission networks with complex authorization structures. We further extend our framework to chain-complete lattices for term structure models and multivalued mappings for complex negotiation systems. Our results demonstrate how lattice theory provides a natural language for understanding complex network dynamics across multiple domains, creating a unified mathematical foundation for analyzing systemic risk, resource allocation, and network *** Codes 91G40, 06B23, 91D30, 68Q85 Copyright © 2025, The Authors. All rights reserved.

关键词： Lattice theory

Hardware Acceleration for HPS Algorithms in Two and Three Dimensions

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arXiv 2025年

作者： Melia, Owen Fortunato, Daniel Hoskins, Jeremy Willett, Rebecca Department of Computer Science University of Chicago United States Center for Computational Mathematics Flatiron Institute United States Center for Computational Biology Flatiron Institute United States Data Science Institute University of Chicago United States Computational and Applied Mathematics Department of Statistics University of Chicago United States

We provide a flexible, open-source framework for hardware acceleration, namely massively-parallel execution on general-purpose graphics processing units (GPUs), applied to the hierarchical Poincaré–Steklov (HPS) family of algorithms for building fast direct solvers for linear elliptic partial differential equations. To take full advantage of the power of hardware acceleration, we propose two variants of HPS algorithms to improve performance on two- and three-dimensional problems. In the two-dimensional setting, we introduce a novel recomputation strategy that minimizes costly data transfers to and from the GPU;in three dimensions, we modify and extend the adaptive discretization technique of Geldermans and Gillman [2019] to greatly reduce peak memory usage. We provide an open-source implementation of these methods written in JAX, a high-level accelerated linear algebra package, which allows for the first integration of a high-order fast direct solver with automatic differentiation tools. We conclude with extensive numerical examples showing our methods are fast and accurate on two- and three-dimensional problems. © 2025, CC BY.

关键词： Graphics processing unit

Accurate Formula for the Effective Conductivity of Highly Clustered Two-Phase Materials

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arXiv 2025年

作者： Skolnick, Murray Torquato, Salvatore Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Chemistry Department of Physics Princeton Materials Institute and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Two-phase heterogeneous materials arising in a variety of natural and synthetic situations exhibit a wide-variety of microstructures and thus display a broad-spectrum effective physical properties. Given that such properties of disordered materials generally depend on an infinite-set of microstructural correlation functions that are typically unobtainable, in practice, one must consider rigorous bounds and approximations on the effective properties that depend on a limited set of nontrivial microstructural information. Torquato derived [Random Heterogeneous Materials, Microstructure and Macroscopic Properties (2002)] such an approximation for the effective thermal/electrical conductivity of two-phase microstructures that perturb about those that realize the well-known self-consistent formula;a key feature of such microstructures being phase-inversion symmetry which can be observed in certain cellular, bicontinuous, and porous materials. Here, we show via extensive numerical simulations that this virtually unexamined approximation, which depends on up to three-point correlations, predicts accurately the effective conductivities of various two- and three-dimensional two-phase microstructures across phase volume fractions in which both phases can form large and well-connected clusters;including certain phase-inversion symmetric microstructures as well as phase-inversion asymmetric dispersions of certain fully penetrable particles. We also highlight the accurate sensitivity of this approximation to phase-connectedness properties by showing that it predicts percolation thresholds that are in good quantitative and/or qualitative agreement with known thresholds for the models considered here. Finally, we discuss how this formula can be used to design materials with desirable effective conductivity properties and thus aid in materials by design. © 2025, CC BY-SA.

关键词： Porous materials

Statistical Inference for Low-Rank Tensor Models

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arXiv 2025年

作者： Xu, Ke Chen, Elynn Han, Yuefeng Department of Applied and Computational Mathematics and Statistics University of Notre Dame United States Department of Technology Operations and Statistics New York University United States

Statistical inference for tensors has emerged as a critical challenge in analyzing high-dimensional data in modern data science. This paper introduces a unified framework for inferring general and low-Tucker-rank linear functionals of low-Tucker-rank signal tensors for several low-rank tensor models. Our methodology tackles two primary goals: achieving asymptotic normality and constructing minimax-optimal confidence intervals. By leveraging a debiasing strategy and projecting onto the tangent space of the low-Tucker-rank manifold, we enable inference for general and structured linear functionals, extending far beyond the scope of traditional entrywise inference. Specifically, in the low-Tucker-rank tensor regression or PCA model, we establish the computational and statistical efficiency of our approach, achieving near-optimal sample size requirements (in regression model) and signal-to-noise ratio (SNR) conditions (in PCA model) for general linear functionals without requiring sparsity in the loading tensor. Our framework also attains both computationally and statistically optimal sample size and SNR thresholds for low-Tucker-rank linear functionals. Numerical experiments validate our theoretical results, showcasing the framework’s utility in diverse applications. This work addresses significant methodological gaps in statistical inference, advancing tensor analysis for complex and high-dimensional data *** Codes 62H10 (Primary) 62H25, 62F12 (Secondary) © 2025, CC BY-NC-SA.

关键词： Tensors