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Portugal, University of Aveiro, Department of Mathematics

The conference DEOTIA26 serves as a premier forum for mathematicians, engineers, and interdisciplinary researchers to share novel insights, theoretical developments, and practical applications that bridge mathematics with real-world problem-solving. The event highlights the transformative power of analytical and numerical methods across diverse domains, emphasizing innovation, efficiency, and sustainability. The Portuguese Mathematical Society (Sociedade Portuguesa de Matemática) endorses DEOTIA26 and its scientific scope: (1) Mathematical Foundations: This thematic area explores the theoretical aspects of differential equations, variational principles, and optimization theory. It focuses on analytical methods and the development of new mathematical theoretical directions; (2) Computational and Numerical Methods: This thematic area centers on algorithmic approaches and computational strategies for solving complex differential and optimization problems. Topics include numerical simulation, machine learning-assisted optimization, and big data computational systems; (3) Solutions and Interdisciplinary Applications: This thematic area focuses on applying mathematical tools to real-world scenarios in engineering, physics, biology, economics, and environmental sciences, with special focus in Logistics and Manufacturing—demonstrating how mathematical modeling drives innovation and decision-making across disciplines.

DEOTIA26 Organizing Committee;     Email: event_deotia26@spm.pt

Differential equations, elliptic PDEs, dispersive equations, calculus of variations, optimal control, geometric optimization, nonlinear evolution equations, Lavrentiev phenomenon, nonstandard growth (Musielak–Orlicz), integro-differential equations, fractional operators, numerical simulation, AI techniques for differential equations, event-driven simulation & optimization, logistics and manufacturing decision-support solutions

Applied Mathematics (in general),    Optimization Theory, Operational Research