Mini-Symposia

This mini-symposium investigates the interaction of computational approaches, such as deep learning, artificial intelligence, parallel computing and quantum computing, with applications in agriculture, epidemiology, and plant pathology. The presentations will focus on research on: deep learning models for molecular diagnostics of plant pathogens; AI-driven interpretation of volatile organic molecules for plant communication; stable numerical methods for the efficient solution of models that give rise to Turing patterns; standard and non-standard numerical methods in epidemic modeling. The symposium will also discuss the use of quantum computers in agricultural research, as well as the employment of epidemiological models to forecast the spread of information on social media. Furthermore, theoretical aspects concerning algorithms used in the context of neural networks will be addressed. This event allows researchers to showcase their cutting-edge discoveries and discuss collaboration prospects to improve agricultural and epidemiological practices.

Evolutionary Partial Differential Equations (PDEs) are crucial for various scientific and engineering fields, serving as the basis for modeling a wide range of phenomena. However, due to the complexity of these equations, obtaining analytical solutions is often impractical, therefore developing efficient numerical techniques for approximating solutions is essential. This requires special attention to designing solvers capable of handling the challenges inherent in evolutionary PDEs, such as dimensionality, stiffness, non-linearities, and time-varying boundary conditions.

The mini-symposium aims to gather young researchers across different disciplines to share recent advancements in efficiently solving evolutionary PDEs. Diverse applications will be considered including uncertainty quantification, optimal control, multi-scale and multi-physics problems, and real-world applications in fields such as crowd dynamics, traf- fic flows and epidemiology.

Hierarchical time series are collections of time series formed via aggregation. For example, the aggregation of the time series of the regional levels of tourism yields the time series of the national level of tourism. Forecasts for hierarchical time series should be coherent: the sum of the forecasts of the regional tourism levels should match the forecast for the national tourism level. The most popular technique to enforce coherence is called reconciliation, which adjusts the base forecasts computed for each time series to satisfy the summing constraints implied by the hierarchy. Forecast coherence is often required for aligned decision making; moreover, reconciliation has been shown to improve the quality of the forecasts. Hierarchical forecasting is a very active and rapidly growing research topic, with several significant applications in different areas, such as energy, retail, and macroeconomics. This mini-symposium will cover recent advancements in different aspects of the field.

This mini-symposium will bring together young applied mathematicians to discuss recent advancements in mathematical modeling and numerical analysis of complex physical systems. The focus will be on Hybrid Boltzmann–BGK Models and Hydrodynamic Limits, which use kinetic theory and fluid dynamics to describe the behavior of gas mixtures and derive macroscopic equations from microscopic interactions. Discussions will also cover Numerical Methods for Kinetic Equations, emphasizing the development and analysis of computational algorithms that ensure high accuracy, stability, conservation, and positivity. Additionally, the Schrödinger-Poisson System will be examined, focusing on quantum mechanical modeling and numerical techniques for solving the Schrödinger equation in quantum devices. Lastly, the symposium will address Classical and Quantum Transport Models, studying transport phenomena using equations like the Wigner equation and exploring optimal control problems in both classical and quantum contexts.

The finite element method (FEM) is a highly versatile technique widely employed for the numerical solution of partial differential equations. In addressing complex scenarios, strategies such as enriched finite element methods (EFEM) and isogeometric analysis (IGA) come to the fore. EFEM involves augmenting the approximation space with suitable enrichment functions, enhancing its capacity to address challenging phenomena like singularities and discontinuities. Conversely, IGA takes advantage of spline-based geometric representations to enhance the integration of geometric design and analysis. These approaches represent significant advancements in computational mechanics and numerica analysis.

This mini-symposium focuses on presenting recent applications and tools that advance both EFEM formulations and robust IGA techniques. It brings together young researchers working in these fields. Specific topics of interest include, but are not limited to: Finite element methods,  Enriched finite element methods, Efficient weighted quadrature rules for isogeometric analysis, B-spline based adaptive isogeometric analysis