SIAM Review最新文献

{"title":"Sandpiles and Dunes: Mathematical Models for Granular Matter","authors":"Piermarco Cannarsa, Stefano Finzi Vita","doi":"10.1137/23m1583673","DOIUrl":"https://doi.org/10.1137/23m1583673","url":null,"abstract":"SIAM Review, Volume 66, Issue 4, Page 751-777, November 2024. <br/> Granular materials are everywhere, in the environment but also in our pantry. Their properties are different from those of any solid material, due to the possibility of sudden phenomena such as avalanches or landslides. Here we present a brief survey on their characteristics and on what can be found (from the past thirty years) in the recent mathematics literature in order to reproduce their behavior. We discuss, in particular, differential models proposed for the growth of a sandpile on a table and, when wind comes into play, for the formation and dynamics of sand dunes. This field of research is still of great interest since there is no consolidated general model for the dynamics of granular matter, but rather only standalone models adapted to specific situations.","PeriodicalId":49525,"journal":{"name":"SIAM Review","volume":"18 1","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SIGEST","authors":"The Editors","doi":"10.1137/24n976006","DOIUrl":"https://doi.org/10.1137/24n976006","url":null,"abstract":"SIAM Review, Volume 66, Issue 4, Page 719-719, November 2024. <br/> The SIGEST article in this issue, “A Bridge between Invariant Theory and Maximum Likelihood Estimation,” by Carlos Améndola, Kathlén Kohn, Philipp Reichenbach, and Anna Seigal, uncovers the deep connections between geometric invariant theory and statistical methods, specifically maximum likelihood estimation (MLE) by connecting it to norm minimization over group orbits. The authors develop a dictionary relating stability notions in geometric invariant theory to the existence and uniqueness of MLEs, which applies to both Gaussian and log-linear models. In comparison to the original 2021 version of the paper that appeared in the SIAM Journal on Applied Algebra and Geometry, for the SIGEST version, the authors added new content on log-linear models, simplified technical proofs, removed detailed appendices, and incorporated new examples and figures for accessibility. In particular, the focus was primarily on Gaussian models, whereas this updated SIGEST version expands the coverage by incorporating results from the authors' companion paper on log-linear models. Furthermore, a new figure (Fig. 1) visually illustrates the two core concepts of invariant theory and MLE. Significant changes include the removal of technical details and appendices to streamline the content and make it more accessible to a broader audience. The introduction of examples, particularly for the Kempf--Ness Theorem, further aids understanding. This paper makes several key contributions of broad mathematical interest. MLE is a key statistical technique that is widely used. Having a new handle on its well-posedness analysis deepens the understanding of the mechanisms behind this technique as well as potentially paves the way to extending existing theory for MLE models. Also, on the computational side, algorithms from the optimization over orbits can be used for MLE, and vice versa, which could possibly lead to new and more efficient algorithms in both fields. Overall, the work beautifully highlights how techniques from one field can be applied to the other, with applications to generalization bounds, group actions, and optimization landscapes. In the last section of their SIGEST paper the authors discuss possible future research directions that capitalize on the dictionary they have uncovered.","PeriodicalId":49525,"journal":{"name":"SIAM Review","volume":"69 1","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Survey and Review","authors":"Marlis Hochbruck","doi":"10.1137/24n975980","DOIUrl":"https://doi.org/10.1137/24n975980","url":null,"abstract":"SIAM Review, Volume 66, Issue 4, Page 617-617, November 2024. <br/> Neural oscillations are periodic activities of neurons in the central nervous system of eumetazoa. In an oscillatory neural network, neurons are modeled by coupled oscillators. Oscillatory networks are employed for describing the behavior of complex systems in biology or ecology with respect to the connectivity of the network components or the nonlinear dynamics of the individual units. Phase-locked periodic states and their instabilities are core features in the analysis of oscillatory networks. In “Oscillatory Networks: Insights from Piecewise-Linear Modeling,” Stephen Coombes, Mustafa Şayli, Rüdiger Thul, Rachel Nicks, Mason A. Porter, and Yi Ming Lai review techniques for studying coupled oscillatory networks. They first discuss phase reductions, phase-amplitude reductions, and the master stability function for smooth dynamical systems. Then they consider nonsmooth piecewise-linear (PWL) systems, for which periodic orbits are easily obtained. Saltation operators are used for modeling the propagation of perturbations through switching manifolds in the analysis of the dynamics and bifurcations at the network level. Applications to neural systems, cardiac systems, networks of electromechanical oscillators, and cooperation in cattle herds illustrate the power of these methods. PWL modeling has been applied for a long time in engineering. Recently, it has been introduced in other fields, such as social sciences, finance, and biology. For many modern applications in science, piecewise models are much more versatile than the classical smooth dynamical systems. In neuroscience, PWL functions enable explicit calculations which are infeasible in the original smooth system. This includes discontinuous dynamical systems, which are used to model impacting mechanical oscillators, integrate-and-fire models of spiking neurons, and cardiac oscillators. On the other hand, the price to pay is the retrieval of new conditions for the existence, uniqueness, and stability of solutions. The paper discusses the application of PWL models to a large variety of applications from engineering and biology. It will be of interest to many readers.","PeriodicalId":49525,"journal":{"name":"SIAM Review","volume":"9 1","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sigmoid Functions, Multiscale Resolution of Singularities, and $hp$-Mesh Refinement","authors":"Daan Huybrechs, Lloyd N. Trefethen","doi":"10.1137/23m1556629","DOIUrl":"https://doi.org/10.1137/23m1556629","url":null,"abstract":"SIAM Review, Volume 66, Issue 4, Page 683-693, November 2024. <br/> In this short, conceptual paper we observe that closely related mathematics applies in four contexts with disparate literatures: (1) sigmoidal and RBF approximation of smooth functions, (2) rational approximation of analytic functions with singularities, (3) $hpkern .7pt$-mesh refinement for solution of pdes, and (4) double exponential (DE) and generalized Gauss quadrature. The relationships start from the change of variables $s = log(x)$, and they suggest possibilities for new analyses and new methods in several areas. Concerning (2) and (3), we show that both problems feature the same effect of “linear tapering” near the singularity---of clustered poles in rational approximation and of polynomial orders in $hpkern .7pt$-mesh refinement. Concerning (4), we note that the tapering effect appears here too, and that the change of variables interpretation sheds new light on why the DE and generalized Gauss methods are effective at integrating arbitrary singularities.","PeriodicalId":49525,"journal":{"name":"SIAM Review","volume":"95 1","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research Spotlights","authors":"Stefan M. Wild","doi":"10.1137/24n975992","DOIUrl":"https://doi.org/10.1137/24n975992","url":null,"abstract":"SIAM Review, Volume 66, Issue 4, Page 681-681, November 2024. <br/> Logarithmic transformations are used broadly in data science, mathematics, and engineering, and yet they can still reveal surprising connections between seemingly unrelated disciplines. This issue's first research spotlight, “Sigmoid Functions, Multiscale Resolution of Singularities, and $hp$-Mesh Refinement,” illuminates how the change of variables $s = log(x)$ connects different areas of computational mathematics. Authors Daan Huybrechs and Lloyd “Nick” Trefethen show new relationships between smooth approximation, rational approximation theory, adaptive mesh refinement, and numerical quadrature. For example, the authors show that this change of variables can be naturally tied to a “linear tapering” effect near singularities, which is a common feature in both rational approximation and $hp$-mesh refinement. Through a number of effective examples, the authors illustrate the power of these relationships across areas that have seen relatively independent lines of development. In doing so, the authors suggest opportunities for developing and analyzing new methods by leveraging the new connections, including mesh refinement strategies, techniques for multivariate approximation, and hybrid approaches that combine the strengths of disparate methods. How well can information be recovered from water waves? This question is at the heart of this issue's second research spotlight, “Feynman's Inverse Problem.” Author Adrian Kirkeby is motivated by a thought experiment posed by the physicist and iconoclast Richard Feynman wherein an insect floating in a swimming pool wants to determine where and when others have jumped into the pool, causing the waves the insect observes. Kirkeby constructs and analyzes a linear 2D-3D system of partial differential equations (PDEs) for the forward model. Leveraging the nonlocality of this system of PDEs, Kirkeby shows conditions under which the insect can determine the source of the waves---in fact, uniquely---simply by observing the wave amplitude and water velocity in any small area of the surface. This model is then extended to capture settings where noisy observations and observations at a finite number of time and space points are collected, and establishes stability properties and error bounds for the reconstruction. The paper concludes with illustrative numerical experiments based on a nonharmonic Fourier inversion method. Kirkeby also highlights several avenues for future research, noting that inverse problems for water or other surface waves have received less attention than those involving acoustic or electromagnetic waves. As an added bonus, the referenced video of Feynman is not to be missed.","PeriodicalId":49525,"journal":{"name":"SIAM Review","volume":"243 1","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bridge between Invariant Theory and Maximum Likelihood Estimation","authors":"Carlos Améndola, Kathlén Kohn, Philipp Reichenbach, Anna Seigal","doi":"10.1137/24m1661753","DOIUrl":"https://doi.org/10.1137/24m1661753","url":null,"abstract":"SIAM Review, Volume 66, Issue 4, Page 721-747, November 2024. <br/> We uncover connections between maximum likelihood estimation in statistics and norm minimization over a group orbit in invariant theory. We present a dictionary that relates notions of stability from geometric invariant theory to the existence and uniqueness of a maximum likelihood estimate. Our dictionary holds for both discrete and continuous statistical models: we discuss log-linear models and Gaussian models, including matrix normal models and directed Gaussian graphical models. Our approach reveals promising consequences of the interplay between invariant theory and statistics. For instance, algorithms from statistics can be used in invariant theory, and vice versa.","PeriodicalId":49525,"journal":{"name":"SIAM Review","volume":"3 1","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing Workforce with Mathematical Modeling Skills","authors":"Ariel Cintrón-Arias, Ryan Andrew Nivens, Anant Godbole, Calvin B. Purvis","doi":"10.1137/19m1277643","DOIUrl":"https://doi.org/10.1137/19m1277643","url":null,"abstract":"SIAM Review, Volume 66, Issue 4, Page 778-792, November 2024. <br/> Mathematicians have traditionally been a select group of academics who produce high-impact ideas enabling substantial results in several fields of science. Throughout the past 35 years, undergraduates enrolling in mathematics or statistics have represented a nearly constant proportion of approximately 1% of bachelor degrees awarded in the United States. Even within STEM majors, mathematics or statistics only constitute about 6% of undergraduate degrees awarded nationally. However, the need for STEM professionals continues to grow, and the list of required occupational skills rests heavily in foundational concepts of mathematical modeling curricula, where the interplay of data, computer simulation, and underlying theoretical frameworks takes center stage. It is not viable to expect a majority of these STEM undergraduates to pursue a double major that includes mathematics. Here we present our solution, some early results of its implementation, and a vision for possible nationwide adoption.","PeriodicalId":49525,"journal":{"name":"SIAM Review","volume":"45 1","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Education","authors":"Hélène Frankowska","doi":"10.1137/24n976018","DOIUrl":"https://doi.org/10.1137/24n976018","url":null,"abstract":"SIAM Review, Volume 66, Issue 4, Page 749-749, November 2024. &lt;br/&gt; In this issue the Education section presents two contributions. The first paper, “Sandpiles and Dunes: Mathematical Models for Granular Matter,” by Piermarco Cannarsa and Stefano Finzi Vita, presents a review of mathematical models for formation of sand piles and dunes. In nature and everyday life various materials appear as conglomerates of particles, like, for instance, sand, gravel, fresh snow, rice, sugar, etc. On larger scales, granular material exhibits new and more complex phenomena which are still not fully understood. It is very different from that of a solid, liquid, or gas in the sense that it can show characteristics similar to one or the other depending on the energy of the system. Its modeling can help in understanding complex natural phenomena such as dune migration, erosion, landslides, and avalanches, and can contribute to the development of environmental protection programs. Such models are also important in various applications in agriculture, construction, energy production, as well as in the chemical, pharmaceutical, food, and metallurgical industries. Even if a sufficiently consolidated general model for the dynamics of granular materials is not available yet, significant progress has been made recently with the introduction of new theoretical models adapted to more specific situations. In this article, after a general description and some historical comments, the authors limit themselves to considering the problem of the growth of a pile of sand on a table under the action of a vertical source of small intensity, neglecting the effects of wind, which has an important role in dune formation. Still, even for such an apparently simpler case, many interesting phenomena do arise and are described in an easily accessible way. Accompanying pictures of real-life experiences make the reading truly enjoyable, and numerical illustrations bring even better intuition on the complexity of phenomena. The authors also indicate literature for further learning. This article is well organized, neatly written, and presents the subject highlighting some of the major aspects. This review of existing models can become a starting point for research projects in a Master's program of applied mathematics and partial differential equations. It could also be used by advanced mathematics students to learn differential models of granular material in an affordable way. The second paper, “Developing Workforce with Mathematical Modeling Skills,” is presented by Ariel Cintrón-Arias, Ryan Andrew Nivens, Anant Godbole and Calvin B. Purvis. Undergraduate mathematics degrees constitute a very small portion of all awarded degrees in the U.S., and this portion is stagnating, while the job growth between 2016 and 2026 for Statisticians and Mathematicians is expected to be substantial. So the need for growth in mathematical training becomes imperative. The authors discuss the nationwide production o","PeriodicalId":49525,"journal":{"name":"SIAM Review","volume":"14 1","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feynman's Inverse Problem","authors":"Adrian Kirkeby","doi":"10.1137/23m1611488","DOIUrl":"https://doi.org/10.1137/23m1611488","url":null,"abstract":"SIAM Review, Volume 66, Issue 4, Page 694-718, November 2024. <br/> We analyze an inverse problem for water waves posed by Richard Feynman in the BBC documentary Fun to Imagine. We show that the problem can be modeled as an inverse Cauchy problem for gravity-capillary waves, conduct a detailed analysis of the Cauchy problem, and give a uniqueness proof for the inverse problem. Somewhat surprisingly, this results in a positive answer to Feynman's question. In addition, we derive stability estimates for the inverse problem for both continuous and discrete measurements, propose a simple inversion method, and conduct numerical experiments to verify our results.","PeriodicalId":49525,"journal":{"name":"SIAM Review","volume":"109 1","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oscillatory Networks: Insights from Piecewise-Linear Modeling","authors":"Stephen Coombes, Mustafa Şayli, Rüdiger Thul, Rachel Nicks, Mason A. Porter, Yi Ming Lai","doi":"10.1137/22m1534365","DOIUrl":"https://doi.org/10.1137/22m1534365","url":null,"abstract":"SIAM Review, Volume 66, Issue 4, Page 619-679, November 2024. <br/> There is enormous interest---both mathematically and in diverse applications---in understanding the dynamics of coupled-oscillator networks. The real-world motivation of such networks arises from studies of the brain, the heart, ecology, and more. It is common to describe the rich emergent behavior in these systems in terms of complex patterns of network activity that reflect both the connectivity and the nonlinear dynamics of the network components. Such behavior is often organized around phase-locked periodic states and their instabilities. However, the explicit calculation of periodic orbits in nonlinear systems (even in low dimensions) is notoriously hard, so network-level insights often require the numerical construction of some underlying periodic component. In this paper, we review powerful techniques for studying coupled-oscillator networks. We discuss phase reductions, phase--amplitude reductions, and the master stability function for smooth dynamical systems. We then focus, in particular, on the augmentation of these methods to analyze piecewise-linear systems, for which one can readily construct periodic orbits. This yields useful insights into network behavior, but the cost is that one needs to study nonsmooth dynamical systems. The study of nonsmooth systems is well developed when focusing on the interacting units (i.e., at the node level) of a system, and we give a detailed presentation of how to use saltation operators, which can treat the propagation of perturbations through switching manifolds, to understand dynamics and bifurcations at the network level. We illustrate this merger of tools and techniques from network science and nonsmooth dynamical systems with applications to neural systems, cardiac systems, networks of electromechanical oscillators, and cooperation in cattle herds.","PeriodicalId":49525,"journal":{"name":"SIAM Review","volume":"145 1","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}