Aims & Scope
Data-Driven Modelling delves into the cutting-edge ideas, methodologies, and tools for constructing data-driven models.Models are the key to understanding reality and making informed decisions. They transform raw data into information and produce actionable insights.
From predicting traffic flow to launching satellites, modelling is woven into our daily lives. Modelling real, complex systems is a skill that modern humans excel in, but our mathematical and statistical tools are still lagging behind, and navigating complexity rests a challenge.
This journal publishes works that explore all diverse forms of data-driven modelling, from foundational mathematical approaches to simulations. We cover the tools that drive discovery and innovation in this ever-evolving field.
The journal encourages authors to submit manuscripts addressing the following topics (other topics are also welcome):
Complex Systems
Complex systems are ubiquitous in nature, from living organisms to markets. They exhibit emergent properties that cannot be explained solely by understanding their individual components. Data-driven modelling of complex systems aims to unravel their intricate interactions and predict their behavior, presenting both challenges and opportunities for understanding the complexity of the world around us.
Disordered Materials
Materials, such as glasses, amorphous solids, and active matter, lack the regular atomic structure of crystalline materials, making them difficult to model. Data-driven modelling offers a promising avenue to capture the intricate structure of disorder in these materials, enabling advancements in material science and engineering for applications ranging from electronics to pharmaceuticals.
Networks
From social networks to biological systems, networks serve as powerful representations of interconnected entities. Data-driven modelling combined with networks seeks to uncover the underlying structure of real complex systems, detecting influential elements, and predicting dynamic behaviors. Understanding network properties has implications for information spread, disease contagion, and transportation and communication networks.
Socio-economic and Financial Systems
Markets and socio-economic systems are highly dynamic and influenced by numerous interconnected factors. Data-driven modelling offers insights into market trends, risk assessment, and economic predictions. By capturing intricate patterns from historic observations, data-driven models hold the potential to enhance decision-making, inform policy, and promote sustainable development in these complex systems.
Mathematical Foundations
At the core of good data-driven modelling lies a strong mathematical foundation. From probability theory to optimization techniques, these mathematical principles underpin the creation and evaluation of data-driven models. Advancements in mathematical tools enable us to extract meaningful information from vast datasets, empowering data-driven modelling across diverse domains.