Skip to main content Accessibility help
×
  1. Home
  2. Browse subjects
  3. Mathematics
  4. Computational Science
  5. Content listing

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

2540 results in Computational Science

Page 1 of 127

Differential Equations and Variational Methods on Graphs
  • With Applications to Machine Learning and Image Analysis
  • Yves van Gennip, Jeremy Budd
  • Coming soon
  • Expected online publication date:
    February 2026
    Print publication:
    28 February 2026
  • The burgeoning field of differential equations on graphs has experienced significant growth in the past decade, propelled by the use of variational methods in imaging and by its applications in machine learning. This text provides a detailed overview of the subject, serving as a reference for researchers and as an introduction for graduate students wishing to get up to speed. The authors look through the lens of variational calculus and differential equations, with a particular focus on graph-Laplacian-based models and the graph Ginzburg-Landau functional. They explore the diverse applications, numerical challenges, and theoretical foundations of these models. A meticulously curated bibliography comprising approximately 800 references helps to contextualise this work within the broader academic landscape. While primarily a review, this text also incorporates some original research, extending or refining existing results and methods.

Minimal Cubature Rules
  • Theory and Practice
  • Yuan Xu
  • Coming soon
  • Expected online publication date:
    November 2025
    Print publication:
    30 November 2025
  • Cubature rules are indispensable tools in scientific computing and applied sciences whenever evaluating or discretizing integrals is needed. This monograph is the first comprehensive resource devoted to cubature rules in English since Stroud's classic 1971 book, and the first book about minimal cubature rules. The book explores the subject's theoretical side, which intersects with many branches of mathematics. Minimal cubature rules are intimately connected with common zeros of orthogonal polynomials, which can be described via the polynomial ideals and varieties. Many prominent or practical cubature rules are invariant under a finite group, and some involve symmetric functions and the discrete Fourier transform. Based on state-of-the-art research, the book systematically studies Gauss and minimal cubature rules, and includes a chapter on the practical aspects of construction cubature rules on triangles and simplexes. This comprehensive guide is ideal for researchers and advanced graduate students across the computational and applied mathematics community.

The Toda Lattice and Universality for the Computation of the Eigenvalues of a Random Matrix
  • Percy Deift, Guillaume Dubach, Carlos Tomei, Thomas Trogdon
  • Coming soon
  • Expected online publication date:
    October 2025
    Print publication:
    31 October 2025
  • Written by leaders in the field, this text showcases some of the remarkable properties of the finite Toda lattice and applies this theory to establish universality for the associated Toda eigenvalue algorithm for random Hermitian matrices. The authors expand on a 2019 course at the Courant Institute to provide a comprehensive introduction to the area, including previously unpublished results. They begin with a brief overview of Hamiltonian mechanics and symplectic manifolds, then derive the action-angle variables for the Toda lattice on symmetric matrices. This text is one of the first to feature a new perspective on the Toda lattice that does not use the Hamiltonian structure to analyze its dynamics. Finally, portions of the above theory are combined with random matrix theory to establish universality for the runtime of the associated Toda algorithm for eigenvalue computation.

Mathematical Methods in Data Science
  • Bridging Theory and Applications with Python
  • Sébastien Roch
  • Coming soon
  • Expected online publication date:
    October 2025
    Print publication:
    31 October 2025
  • Bridge the gap between theoretical concepts and their practical applications with this rigorous introduction to the mathematics underpinning data science. It covers essential topics in linear algebra, calculus and optimization, and probability and statistics, demonstrating their relevance in the context of data analysis. Key application topics include clustering, regression, classification, dimensionality reduction, network analysis, and neural networks. What sets this text apart is its focus on hands-on learning. Each chapter combines mathematical insights with practical examples, using Python to implement algorithms and solve problems. Self-assessment quizzes, warm-up exercises and theoretical problems foster both mathematical understanding and computational skills. Designed for advanced undergraduate students and beginning graduate students, this textbook serves as both an invitation to data science for mathematics majors and as a deeper excursion into mathematics for data science students.

An Introduction to Immersed Boundary Methods
  • Roberto Verzicco, Marco D. de Tullio, Francesco Viola
  • Coming soon
  • Expected online publication date:
    October 2025
    Print publication:
    31 October 2025
  • Unlock the potential of computational fluid dynamics with this essential guide for master's and graduate students, and researchers. It explores the immersed boundary method (IBM), a revolutionary approach for simulating flows in complex geometries. With a focus on fluid/structure interaction, it examines theoretical principles and practical implementations, offering insights into tackling intricate geometries and enhancing simulation accuracy. The book features a series of numerical examples that increase in complexity, and is accompanied by the source code, allowing readers to replicate results and deepen their understanding. Whether you're wanting to refine your skills or embark on new research, this introduction will empower you to master the art of complex flow simulations.

Page 1 of 127