The Stochastic Modeling and Uncertainty Quantification Conference organized by the Thematic Committee Modelagem Estocástica, Estatística e Quantificação de Incertezas of the Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC) and the Instituto Universitario de Matemática Multidisciplinar (IMM) of the Universitat Politècnica de València (UPV), will be held in València (Spain) from July 8th to July 9th, 2024. You can participate face-to-face in Valencia or online. The conference will be held in Building 1E of Universitat Politècnica de València.

This conference is connected with the XXVI editon of the Mathematical Modelling in Engineering and Human Behaviour Congress 2024 (MME&HB 2024) that will be held in the same location on July 10th to July 12th, 2024. Authors are invited to attend both conferences with a reduced price.

Mathematical models are powerful tools used to describe real-world phenomena. In a rough classification, they can be divided into deterministic and probabilistic-based models. For many years, deterministic models have been the most widely used. Over the last few decades, probabilistic-based models have been introduced to study real-world problems, considering the uncertainty often present in many phenomena.

Uncertainty sources can be found everywhere because 1) Mathematical models are usually simplifications of real-world phenomena; 2) Measurement devices' errors or counting processes may involve errors, which implies that uncertainty should be considered; etc.

In this way, uncertainty must be quantified as part of the modeling process. To this end, specific techniques have already been designed successfully, but there are still many open random/stochastic challenges unsolved within the setting of mathematical modeling with uncertainties. Hence, the main aim of the Stochastic Modeling and Uncertainty Quantification (SMUQ) conference is to be an interdisciplinary forum where experts from different areas can share new ideas and advances on uncertainty quantification techniques and their applications to mathematical modeling using understandable language.

Some potential topics include but are not limited to:

• Stochastic differential and difference equations..
• Random differential and difference equations.
• Agent-based probabilistic models..
• Model calibration techniques. Inverse stochastic problems.
• Control on stochastic/random models.
• Uncertainty Quantification techniques applied to real-world problems: Full stories
• Classical and fractional stochastic processes.
• Random graph models.