Acta NumericaPub Date : 2024-09-04DOI: 10.1017/s0962492923000077
Sergio Blanes, Fernando Casas, Ander Murua
{"title":"Splitting methods for differential equations","authors":"Sergio Blanes, Fernando Casas, Ander Murua","doi":"10.1017/s0962492923000077","DOIUrl":"https://doi.org/10.1017/s0962492923000077","url":null,"abstract":"<p>This overview is devoted to splitting methods, a class of numerical integrators intended for differential equations that can be subdivided into different problems easier to solve than the original system. Closely connected with this class of integrators are composition methods, in which one or several low-order schemes are composed to construct higher-order numerical approximations to the exact solution. We analyse in detail the order conditions that have to be satisfied by these classes of methods to achieve a given order, and provide some insight about their qualitative properties in connection with geometric numerical integration and the treatment of highly oscillatory problems. Since splitting methods have received considerable attention in the realm of partial differential equations, we also cover this subject in the present survey, with special attention to parabolic equations and their problems. An exhaustive list of methods of different orders is collected and tested on simple examples. Finally, some applications of splitting methods in different areas, ranging from celestial mechanics to statistics, are also provided.</p>","PeriodicalId":48863,"journal":{"name":"Acta Numerica","volume":"31 1","pages":""},"PeriodicalIF":14.2,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130832","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}
Acta NumericaPub Date : 2024-09-04DOI: 10.1017/s0962492923000065
Patrick L. Combettes
{"title":"The geometry of monotone operator splitting methods","authors":"Patrick L. Combettes","doi":"10.1017/s0962492923000065","DOIUrl":"https://doi.org/10.1017/s0962492923000065","url":null,"abstract":"<p>We propose a geometric framework to describe and analyse a wide array of operator splitting methods for solving monotone inclusion problems. The initial inclusion problem, which typically involves several operators combined through monotonicity-preserving operations, is seldom solvable in its original form. We embed it in an auxiliary space, where it is associated with a surrogate monotone inclusion problem with a more tractable structure and which allows for easy recovery of solutions to the initial problem. The surrogate problem is solved by successive projections onto half-spaces containing its solution set. The outer approximation half-spaces are constructed by using the individual operators present in the model separately. This geometric framework is shown to encompass traditional methods as well as state-of-the-art asynchronous block-iterative algorithms, and its flexible structure provides a pattern to design new ones.</p>","PeriodicalId":48863,"journal":{"name":"Acta Numerica","volume":"25 1","pages":""},"PeriodicalIF":14.2,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130834","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}
Acta NumericaPub Date : 2024-09-04DOI: 10.1017/s0962492923000089
Tim De Ryck, Siddhartha Mishra
{"title":"Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning","authors":"Tim De Ryck, Siddhartha Mishra","doi":"10.1017/s0962492923000089","DOIUrl":"https://doi.org/10.1017/s0962492923000089","url":null,"abstract":"<p>Physics-informed neural networks (PINNs) and their variants have been very popular in recent years as algorithms for the numerical simulation of both forward and inverse problems for partial differential equations. This article aims to provide a comprehensive review of currently available results on the numerical analysis of PINNs and related models that constitute the backbone of physics-informed machine learning. We provide a unified framework in which analysis of the various components of the error incurred by PINNs in approximating PDEs can be effectively carried out. We present a detailed review of available results on approximation, generalization and training errors and their behaviour with respect to the type of the PDE and the dimension of the underlying domain. In particular, we elucidate the role of the regularity of the solutions and their stability to perturbations in the error analysis. Numerical results are also presented to illustrate the theory. We identify training errors as a key bottleneck which can adversely affect the overall performance of various models in physics-informed machine learning.</p>","PeriodicalId":48863,"journal":{"name":"Acta Numerica","volume":"18 1","pages":""},"PeriodicalIF":14.2,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130835","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}
Acta NumericaPub Date : 2024-09-04DOI: 10.1017/s0962492924000011
Andrea Bonito, Claudio Canuto, Ricardo H. Nochetto, Andreas Veeser
{"title":"Adaptive finite element methods","authors":"Andrea Bonito, Claudio Canuto, Ricardo H. Nochetto, Andreas Veeser","doi":"10.1017/s0962492924000011","DOIUrl":"https://doi.org/10.1017/s0962492924000011","url":null,"abstract":"<p>This is a survey of the theory of adaptive finite element methods (AFEMs), which are fundamental to modern computational science and engineering but whose mathematical assessment is a formidable challenge. We present a self-contained and up-to-date discussion of AFEMs for linear second-order elliptic PDEs and dimension <span>d > 1</span>, with emphasis on foundational issues. After a brief review of functional analysis and basic finite element theory, including piecewise polynomial approximation in graded meshes, we present the core material for coercive problems. We start with a novel <span>a posteriori</span> error analysis applicable to rough data, which delivers estimators fully equivalent to the solution error. They are used in the design and study of three AFEMs depending on the structure of data. We prove linear convergence of these algorithms and rate-optimality provided the solution and data belong to suitable approximation classes. We also address the relation between approximation and regularity classes. We finally extend this theory to discontinuous Galerkin methods as prototypes of non-conforming AFEMs, and beyond coercive problems to inf-sup stable AFEMs.</p>","PeriodicalId":48863,"journal":{"name":"Acta Numerica","volume":"59 1","pages":""},"PeriodicalIF":14.2,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130833","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}
Acta NumericaPub Date : 2024-09-04DOI: 10.1017/s0962492923000053
Jean B. Lasserre
{"title":"The Moment-SOS hierarchy: Applications and related topics","authors":"Jean B. Lasserre","doi":"10.1017/s0962492923000053","DOIUrl":"https://doi.org/10.1017/s0962492923000053","url":null,"abstract":"<p>The Moment-SOS hierarchy, first introduced in optimization in 2000, is based on the theory of the <span>S</span>-moment problem and its dual counterpart: polynomials that are positive on <span>S</span>. It turns out that this methodology can also be used to solve problems with positivity constraints ‘<span>f</span>(x) ≥ 0 for all <span><span><span data-mathjax-type=\"texmath\"><span>$mathbf{x}in S$</span></span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20240904050110862-0140:S0962492923000053:S0962492923000053_inline1.png\"/></span></span>’ or linear constraints on Borel measures. Such problems can be viewed as specific instances of the <span>generalized moment problem</span> (GMP), whose list of important applications in various domains of science and engineering is almost endless. We describe this methodology in optimization and also in two other applications for illustration. Finally we also introduce the Christoffel function and reveal its links with the Moment-SOS hierarchy and positive polynomials.</p>","PeriodicalId":48863,"journal":{"name":"Acta Numerica","volume":"51 1","pages":""},"PeriodicalIF":14.2,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130837","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}
Acta NumericaPub Date : 2024-09-04DOI: 10.1017/s0962492924000023
Xun Huan, Jayanth Jagalur, Youssef Marzouk
{"title":"Optimal experimental design: Formulations and computations","authors":"Xun Huan, Jayanth Jagalur, Youssef Marzouk","doi":"10.1017/s0962492924000023","DOIUrl":"https://doi.org/10.1017/s0962492924000023","url":null,"abstract":"<p>Questions of ‘how best to acquire data’ are essential to modelling and prediction in the natural and social sciences, engineering applications, and beyond. Optimal experimental design (OED) formalizes these questions and creates computational methods to answer them. This article presents a systematic survey of modern OED, from its foundations in classical design theory to current research involving OED for complex models. We begin by reviewing criteria used to formulate an OED problem and thus to encode the goal of performing an experiment. We emphasize the flexibility of the Bayesian and decision-theoretic approach, which encompasses information-based criteria that are well-suited to nonlinear and non-Gaussian statistical models. We then discuss methods for estimating or bounding the values of these design criteria; this endeavour can be quite challenging due to strong nonlinearities, high parameter dimension, large per-sample costs, or settings where the model is implicit. A complementary set of computational issues involves optimization methods used to find a design; we discuss such methods in the discrete (combinatorial) setting of observation selection and in settings where an exact design can be continuously parametrized. Finally we present emerging methods for sequential OED that build non-myopic design policies, rather than explicit designs; these methods naturally adapt to the outcomes of past experiments in proposing new experiments, while seeking coordination among all experiments to be performed. Throughout, we highlight important open questions and challenges.</p>","PeriodicalId":48863,"journal":{"name":"Acta Numerica","volume":"6 1","pages":""},"PeriodicalIF":14.2,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130836","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}
Acta NumericaPub Date : 2023-05-01DOI: 10.1017/S0962492922000101
S. Boldo, C. Jeannerod, G. Melquiond, Jean-Michel Muller
{"title":"Floating-point arithmetic","authors":"S. Boldo, C. Jeannerod, G. Melquiond, Jean-Michel Muller","doi":"10.1017/S0962492922000101","DOIUrl":"https://doi.org/10.1017/S0962492922000101","url":null,"abstract":"Floating-point numbers have an intuitive meaning when it comes to physics-based numerical computations, and they have thus become the most common way of approximating real numbers in computers. The IEEE-754 Standard has played a large part in making floating-point arithmetic ubiquitous today, by specifying its semantics in a strict yet useful way as early as 1985. In particular, floating-point operations should be performed as if their results were first computed with an infinite precision and then rounded to the target format. A consequence is that floating-point arithmetic satisfies the ‘standard model’ that is often used for analysing the accuracy of floating-point algorithms. But that is only scraping the surface, and floating-point arithmetic offers much more. In this survey we recall the history of floating-point arithmetic as well as its specification mandated by the IEEE-754 Standard. We also recall what properties it entails and what every programmer should know when designing a floating-point algorithm. We provide various basic blocks that can be implemented with floating-point arithmetic. In particular, one can actually compute the rounding error caused by some floating-point operations, which paves the way to designing more accurate algorithms. More generally, properties of floating-point arithmetic make it possible to extend the accuracy of computations beyond working precision.","PeriodicalId":48863,"journal":{"name":"Acta Numerica","volume":"32 1","pages":"203 - 290"},"PeriodicalIF":14.2,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44272147","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}
Acta NumericaPub Date : 2023-05-01DOI: 10.1017/S0962492922000095
L. Beirão Da Veiga, F. Brezzi, L. D. Marini, A. Russo
{"title":"The virtual element method","authors":"L. Beirão Da Veiga, F. Brezzi, L. D. Marini, A. Russo","doi":"10.1017/S0962492922000095","DOIUrl":"https://doi.org/10.1017/S0962492922000095","url":null,"abstract":"The present review paper has several objectives. Its primary aim is to give an idea of the general features of virtual element methods (VEMs), which were introduced about a decade ago in the field of numerical methods for partial differential equations, in order to allow decompositions of the computational domain into polygons or polyhedra of a very general shape. Nonetheless, the paper is also addressed to readers who have already heard (and possibly read) about VEMs and are interested in gaining more precise information, in particular concerning their application in specific subfields such as \u0000${C}^1$\u0000 -approximations of plate bending problems or approximations to problems in solid and fluid mechanics.","PeriodicalId":48863,"journal":{"name":"Acta Numerica","volume":"32 1","pages":"123 - 202"},"PeriodicalIF":14.2,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44141488","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}
Acta NumericaPub Date : 2023-05-01DOI: 10.1017/s0962492923000028
Colin J. Cotter
{"title":"Compatible finite element methods for geophysical fluid dynamics","authors":"Colin J. Cotter","doi":"10.1017/s0962492923000028","DOIUrl":"https://doi.org/10.1017/s0962492923000028","url":null,"abstract":"This article surveys research on the application of compatible finite element methods to large-scale atmosphere and ocean simulation. Compatible finite element methods extend Arakawa’s C-grid finite difference scheme to the finite element world. They are constructed from a discrete de Rham complex, which is a sequence of finite element spaces linked by the operators of differential calculus. The use of discrete de Rham complexes to solve partial differential equations is well established, but in this article we focus on the specifics of dynamical cores for simulating weather, oceans and climate. The most important consequence of the discrete de Rham complex is the Hodge–Helmholtz decomposition, which has been used to exclude the possibility of several types of spurious oscillations from linear equations of geophysical flow. This means that compatible finite element spaces provide a useful framework for building dynamical cores. In this article we introduce the main concepts of compatible finite element spaces, and discuss their wave propagation properties. We survey some methods for discretizing the transport terms that arise in dynamical core equation systems, and provide some example discretizations, briefly discussing their iterative solution. Then we focus on the recent use of compatible finite element spaces in designing structure preserving methods, surveying variational discretizations, Poisson bracket discretizations and consistent vorticity transport.","PeriodicalId":48863,"journal":{"name":"Acta Numerica","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135504227","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}