This session covers Linear Algebra, Matrix Analysis, Matrix Theory and their applications in diverse contexts. It aims to span a broad range of interests, including purely algebraic or analytic aspects as well as numerical, combinatorial, geometric, probabilistic, educational or historical ones. It also considers applications of Linear Algebra in areas such as (but not restricted to) Generalized Inverse Matrices, Algebraic Logic, Graph Theory, Optimization, Cryptology, Control Theory, Coding Theory, Structural Mechanics, Signal and Image Processing, Data Mining, and possible extensions to Hilbert/Banach spaces.

The session covers work on the development and analysis of numerical methods across branches of mathematics and their applications, including: numerical solution of ODEs; numerical solution of PDEs; scientific computing and algorithms; stochastic differential equations; approximation theory; numerical linear algebra; numerical solution of integral equations; error analysis and interval analysis; difference equations and recurrence relations; numerical problems in dynamical systems; applications to science (computational physics, statistics, chemistry, engineering, and others); differential algebraic equations; and numerical methods in Fourier analysis.

This session addresses theoretical aspects and numerical methods in control and optimal control of both ordinary and partial differential equations. Topics include (but are not limited to) minimax problems; numerical solution of HJB equations; optimal control problems with singular arcs; mean field games; real-time optimal control of systems; variational formulations; and regularization.

Contributions to this session may address theoretical aspects of the various branches of differential geometry, or the application of differential geometry to engineering, physics and other sciences.

Multicriteria analysis, heuristics and metaheuristics, combinatorial optimization, stochastic optimization, nonlinear optimization, robust optimization, dynamic programming, linear programming, integer linear programming, simulation, graph theory and game theory.

The Optimization session is aimed at researchers and users of optimization, with emphasis on continuous optimization. New theoretical results, algorithm development and applications of new strategies are all welcome.

This session is a space to share both theoretical and numerical-computational results on models arising in physics, engineering, biology and other fields. Specifically, it presents advances and new developments in complementary aspects of differential equations: stationary problems — computation and/or estimation of the spectrum, characterization and/or computation of ground states, influence of the geometry of the domain or the boundary; and evolution problems — computation and/or characterization of the dynamics, stability and parameter analysis, and the predictive capacity of the model.

This session invites contributions in Computational Mechanics, combining theoretical and applied physics with computer science, software engineering and mathematics. Emphasis is placed on the mathematical aspects of new numerical solutions to complex problems in engineering, environmental, medical and other sciences, including but not limited to: computational fluid mechanics, multiphase systems and microfluidics; computational solid mechanics, structures, multibody systems, materials and geomechanics; multiphysics — coupled electromagnetic, thermal, fluid and structural interactions and co-simulations; pre-processing, meshing and post-processing of numerical simulations; formulations and numerical solvers for computational mechanics; parallel and cloud computing; integration of machine learning and artificial intelligence into computational mechanics; and scientific and industrial solutions with open-source and proprietary software.

This session addresses the study of deformable continuous media such as solids, fluids and rheological materials, and includes the development of mathematical techniques and methodologies for use in continuum mechanics. It welcomes contributions with theoretical, numerical and experimental developments in continuum mechanics in which mathematical advances and applications play a significant role.

Free boundary problems generally involve partial differential equations satisfied by certain functions on domains that depend on the solution itself. They arise naturally in physics, industry, biology and optimal design, and raise questions that remain the subject of current research. This session welcomes advances in the study of such problems, both theoretical and applied, as well as the development of mathematical methods for treating them.

Since the pioneering work of Poincaré and Birkhoff, the theory of dynamical systems has grown continuously, producing a wide variety of mathematical techniques. It has always developed in close connection with other disciplines, exchanging problems, techniques and applications. In recent decades there has been growing interest in nonlinear dynamical systems, in computational tools to study them, and in applying those tools to real-world problems in physics, biology, ecology, economics, engineering and computer science, among others. The session covers dynamical systems with continuous or discrete time evolution, linear or nonlinear models, and their simulation and application to physical systems.

The session aims to disseminate new developments in the theoretical, numerical and computational modelling of heat and mass transport phenomena: heat conduction, diffusion of chemical species, forced, natural and mixed convection, melting, solidification, evaporation, boiling and condensation, thermal radiation, transport in porous media, thermal insulation systems, cooling of components and thermophysical properties of materials — as well as their application to industrial processes in mechanical, chemical, agricultural, food and environmental engineering and any other discipline involving heat and mass transfer.

This session presents work on mathematical models for machine learning, supervised and unsupervised classification and regression; neural network architectures including ANN, GNN and STGNN; and numerical methods for training them.

Topics covered: the use of GPU platforms to accelerate computation; distribution of large problems across distributed platforms; computation acceleration techniques; and efficient mathematical libraries on dedicated hardware.

The session welcomes contributions from a range of disciplines that use mathematical or mathematical-computational models and show some form of originality in the construction of the models, whether or not that originality derives from the mathematical modelling itself.

Description inherited from the former combined session “Industrial Mathematics and Interdisciplinary Mathematical Models”; pending review.

The session welcomes contributions from a range of disciplines that use mathematical or mathematical-computational models and show some form of originality in the construction of the models, whether or not that originality derives from the mathematical modelling itself.

This session is a space to share results on the methodological development, processing, interpretation and dissemination of the information contained in signals and images. Specifically, it presents advances in: statistical or mathematical models for processing, signal filtering algorithms, image segmentation, feature extraction, pattern recognition techniques, deep learning models and remote sensing. Contributions are welcome from across science and technology presenting results from the processing of aerial and/or satellite imagery, medical images, atmospheric signals, biomedical signals, audio signals and others. This includes the analysis and validation of model parameters and applications in real or simulated experimental settings.

Solutions that automatically derive a high-level understanding from digital images or video, including: image enhancement, image augmentation, feature extraction, pattern identification and image registration, among others.

This session presents work on mathematics applied to problems related to communicable diseases such as COVID, dengue and Chagas, as well as work on animal and plant diseases. Without being exhaustive: epidemiological evolution models, parameter estimation for such models, and control strategies applied to them.

The Biomathematics session aims to communicate applications of mathematical techniques in the life sciences. It ranges from the use of simple tools on biologically significant problems to more advanced mathematics explaining complex models of biological processes, as well as problems requiring interaction between mathematics and the life sciences.

Topics of the session: game theory, two-sided matching models, social choice theory, strategy-proof allocation mechanisms, general equilibrium theory, economic networks, and mathematical models applied to economics.

The Quantitative Finance session provides a space to discuss original research on: computational finance (both the development and implementation of new methods and algorithms, and their application to specific financial problems); machine learning in finance (a subfield of computational finance that, given its recent growth, is given its own space); financial mathematics, differential equations and stochastic calculus oriented to finance; financial models (portfolio management, risk management, valuation of financial and non-financial assets, and algorithmic trading); and probability, statistics and econometrics applied to finance.

The session accepts two categories of contributions. Research reports: the enquiry must address the topic of the session, and the paper must include the research problem and questions, objectives, methodology, results or progress, conclusions and references. Classroom reflections and experiences: teaching experiences at any educational level developed around the topic of the session; the paper must include the rationale for the proposal, a description of it, the participants and its implementation.

Statistical methods, high-dimensional statistics, Bayesian statistics, computational statistics, probability and stochastic processes.

This session covers inverse problems in general: their theoretical analysis, the implementation of numerical algorithms to recover the required information in each case, and applications of such problems in science and industry. It includes parameter identification and regularization for ill-posed problems.