检索结果-内蒙古大学图书馆

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

学校读者我要写书评

暂无评论

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

Antithetic Multilevel Particle Filters

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Jasra, Ajay Maama, Mohamed Ombao, Hernando Applied Mathematics and Computational Science Program United States Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we consider the filtering of partially observed multi-dimensional diffusion processes that are observed regularly at discrete times. This is a challenging problem which requires the use of advanced numerical schemes based upon time-discretization of the diffusion process and then the application of particle filters. Perhaps the state-of-the-art method for moderate dimensional problems is the multilevel particle filter of [12]. This is a method that combines multilevel Monte Carlo and particle filters. The approach in that article is based intrinsically upon an Euler discretization method. We develop a new particle filter based upon the antithetic truncated Milstein scheme of [10]. We show that for a class of diffusion problems, for ϵ > 0 given, that the cost to produce a mean square error (MSE) in estimation of the filter, of O(ϵ2) is O(ϵ−2 log(ϵ)2). In the case of multidimensional diffusions with non-constant diffusion coefficient, the method of [12] has a cost of O(ϵ−2.5) to achieve the same MSE. We support our theory with numerical results in several examples. Copyright © 2023, The Authors. All rights reserved.

关键词： Mean square error

Unbiased Parameter Estimation for Partially Observed Diffusions

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Awadelkarim, Elsiddig Jasra, Ajay Ruzayqat, Hamza Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this article we consider the estimation of static parameters for partially observed diffusion process with discrete-time observations over a fixed time interval. In particular, we assume that one must time-discretize the partially observed diffusion process and work with the model with bias and consider maximizing the resulting log-likelihood. Using a novel double randomization scheme, based upon Markovian stochastic approximation we develop a new method to unbiasedly estimate the static parameters, that is, to obtain the maximum likelihood estimator with no time discretization bias. Under assumptions we prove that our estimator is unbiased and investigate the method in several numerical examples, showing that it can empirically out-perform existing unbiased *** Codes 60J22, 62M05, 65C40, 62M20 © 2023, CC BY.

关键词： Parameter estimation

A Flow Artist for High-Dimensional Cellular Data

学校读者我要写书评

暂无评论

A Flow Artist for High-Dimensional Cellular Data

IEEE Workshop on Machine Learning for Signal Processing

作者： Kincaid MacDonald Dhananjay Bhaskar Guy Thampakkul Nhi Nguyen Joia Zhang Michael Perlmutter Ian Adelstein Smita Krishnaswamy Department of Mathematics Yale University Department of Genetics Yale School of Medicine Department of Computer Science Yale University Department of Mathematics Pomona College Applied Mathematics Program Yale University Department of Statistics University of Washington Department of Mathematics Boise State University Computational Biology and Bioinformatics Program Yale University

We consider the problem of embedding point cloud data sampled from an underlying manifold with an associated flow or velocity. Such data arises in many contexts where static snapshots of dynamic entities are measured, including in high-throughput biology such as single-cell transcriptomics. Existing embedding techniques either do not utilize velocity information or embed the coordinates and velocities independently, i.e., they either impose velocities on top of an existing point embedding or embed points within a prescribed vector field. Here we present FlowArtist, a neural network that embeds points while jointly learning a vector field around the points. The combination allows FlowArtist to better separate and visualize velocity-informed structures. Our results, on toy datasets and single-cell RNA velocity data, illustrate the value of utilizing coordinate and velocity information in tandem for embedding and visualizing high-dimensional data.

关键词：

Inferring System and Optimal Control Parameters of Closed-Loop Systems from Partial Observations

学校读者我要写书评

暂无评论

Inferring System and Optimal Control Parameters of Closed-Lo...

IEEE Conference on Decision and Control

作者： Victor Geadah Juncal Arbelaiz Harrison Ritz Nathaniel D. Daw Jonathan D. Cohen Jonathan W. Pillow Program in Applied and Computational Mathematics Princeton University Princeton NJ USA Department of Mechanical and Aerospace Engineering Princeton University Princeton NJ USA Princeton Neuroscience Institute Princeton University Princeton NJ USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We consider the joint problem of system identification and inverse optimal control for discrete-time stochastic Linear Quadratic Regulators. We analyze finite and infinite time horizons in a partially observed setting, where the state is observed noisily. To recover closed-loop system parameters, we develop inference methods based on probabilistic statespace model (SSM) techniques. First, we show that the system parameters exhibit non-identifiability in the infinite-horizon from closed-loop measurements, and we provide exact and numerical methods to disentangle the parameters. Second, to improve parameter identifiability, we show that we can further enhance recovery by either (1) incorporating additional partial measurements of the control signals or (2) moving to the finitehorizon setting. We further illustrate the performance of our methodology through numerical examples.

关键词： Regulators Optimal control Stochastic processes Probabilistic logic System identification Numerical models Closed loop systems

Scalable Cluster-Consistency Statistics for Robust Multi-Object Matching

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Shi, Yunpeng Li, Shaohan Maunu, Tyler Lerman, Gilad Program in Applied and Computational Mathematics Princeton University United States School of Mathematics University of Minnesota United States Department of Mathematics Brandeis University United States

We develop new statistics for robustly filtering corrupted keypoint matches in the structure from motion pipeline. The statistics are based on consistency constraints that arise within the clustered structure of the graph of keypoint matches. The statistics are designed to give smaller values to corrupted matches and than uncorrupted matches. These new statistics are combined with an iterative reweighting scheme to filter keypoints, which can then be fed into any standard structure from motion pipeline. This filtering method can be efficiently implemented and scaled to massive datasets as it only requires sparse matrix multiplication. We demonstrate the efficacy of this method on synthetic and real structure from motion datasets and show that it achieves state-of-the-art accuracy and speed in these tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Statistics

Comparison between a priori and a posteriori slope limiters for high-order finite volume schemes

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Palafoutas, Jonathan Velasco Romero, David A. Teyssier, Romain Program in Applied & Computational Mathematics Princeton University Washington Road PrincetonNJ08544 United States Department of Astrophysical Sciences Princeton University 171 Broadmead Street PrincetonNJ08540 United States

High-order finite volume and finite element methods offer impressive accuracy and cost efficiency when solving hyperbolic conservation laws with smooth solutions. However, if the solution contains discontinuities, these high-order methods can introduce unphysical oscillations and severe overshoots/undershoots. Slope limiters are an effective remedy, combating these oscillations by preserving monotonicity. Some limiters can even maintain a strict maximum principle in the numerical solution. They can be classified into one of two categories: a priori and a posteriori limiters. The former revises the high-order solution based only on data at the current time tn, while the latter involves computing a candidate solution at tn+1 and iteratively recomputing it until some conditions are satisfied. These two limiting paradigms are available for both finite volume and finite element methods. In this work, we develop a methodology to compare a priori and a posteriori limiters for finite volume solvers at arbitrarily high order. We select the maximum principle preserving scheme presented in [1, 2] as our a priori limited scheme. For a posteriori limiting, we adopt the methodology presented in [3] and search for so-called troubled cells in the candidate solution. We revise them with a robust MUSCL fallback scheme. The linear advection equation is solved in both one and two dimensions and we compare variations of these limited schemes based on their ability to maintain a maximum principle, solution quality over long time integration and computational cost. This analysis reveals a fundamental tradeoff between these three aspects. The high-order a posteriori limited solutions boast great quality at long time-scales, taking full advantage of the sharp gradients of the high-order finite volume method. However, they introduce consistent maximum principle violations. On the other hand, the high-order a priori limited solutions can preserve a strict maximum principle. Interestingly, thi

关键词： Polynomials

Cosserat elasticity as the weak-field limit of Einstein–Cartan relativity

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Maitra, Matthew Tromp, Jeroen Institut für Geophysik ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Geosciences and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States

The weak-field limit of Einstein–Cartan (EC) relativity is studied. The equations of EC theory are rewritten such that they formally resemble those of Einstein General Relativity (EGR);this allows ideas from post-Newtonian theory to be imported without essential change. The equations of motion are then written both at first post-Newtonian (1PN) order and at 1.5PN order. EC theory’s 1PN equations of motion are found to be those of a micropolar/Cosserat elastic medium, along with a decoupled evolution equation for non-classical, spin-related fields. It seems that a necessary condition for these results to hold is that one chooses the non-classical fields to scale with the speed of light in a certain empirically reasonable way. Finally, the 1.5PN equations give greater insight into the coupling between energy-momentum and spin within slowly moving, weakly gravitating matter. Specifically, the weakly relativistic modifications to Cosserat theory involve a gravitational torque and an augmentation of the gravitational force due to a ‘dynamic mass moment density’ with an accompanying ‘dynamic mass moment density flux’, and new forms of linear momentum density captured by a ‘dynamic mass density flux’ and a ‘dynamic momentum density’. Copyright © 2024, The Authors. All rights reserved.

关键词： Equations of motion

DeePKS Model for Halide Perovskites with the Accuracy of Hybrid Functional

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Ou, Qi Tuo, Ping Li, Wenfei Wang, Xiaoxu Chen, Yixiao Zhang, Linfeng AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Accurate prediction for the electronic structure properties of halide perovskites plays a significant role in the design of highly efficient and stable solar cells. While density functional theory (DFT) within the generalized gradient approximation (GGA) offers reliable prediction in terms of lattice constants and potential energy surface for halide perovskites, it severely underestimates the band gap due to the lack of non-local exact exchange term, which exists in computationally expensive hybrid functionals. In this work, a universal Deep Kohn-Sham (DeePKS) model based on neural network is trained so as to enable electronic structure calculations with the accuracy of hybrid functional HSE06 and the efficiency comparable to GGA functional, for a plethora of halide perovskites, i.e., ABX3 (A=FA, MA, Cs;B=Sn, Pb;X=Cl, Br, I). Forces, band gaps, and density of states (DOS) predicted by our DeePKS model for all aforementioned perovskites are in good agreement with the HSE06 results, with significantly improved efficiency. In addition, even though the spin-orbit coupling (SOC) effect has not been taken into consideration during the training process, DeePKS+SOC offers highly consistent band gap and DOS as compared to HSE06+SOC for Pb-containing systems. We believe such DeePKS model can be readily applied for an accurate yet efficient prediction of various properties for the family of halide perovskites. © 2023, CC BY.

关键词： Energy gap