Complex systems (CS) involve multiple interactive parts featuring the generation of a new quality of collective behaviors by self-organization with the outcomes of spontaneous formation of temporal, spatial or functional structures. CS’ properties stem from non-linear interaction of constituents, and the approaches to address CSs and uncertainty span across the entire deterministic-stochastic-discrete-continuous mathematical approximations and principled approaches.
Complexity theory along with complexity serves as a bridge crossing over the quantitative and qualitative facets of life, which enables comprehensive contemplation of diverse systems, which are possible to be comprehended partially by traditional scientific methods merely. Complexity reflects disentangling of complex, dynamic, nonlinear systems, amongst many more. Complexity also provides facilitation in observing problems through multiple perspectives, examining micro and macro issues. Therefore, complexity science discovers the underlying principles, theoretical aspects of emergence oriented by using them through applications so biological, physical and social worlds are understood at the pedestal of emergence of chaos-order as the hallmarks of natural and designed systems.
Data science within an interdisciplinary approach includes extracting significant insights concerning big data, with governing principles and applications of different areas to be adopted and implemented. The mathematics of data encompasses a multifaceted blend of mathematical techniques and models, which denotes its pivotal role in tackling voluminous datasets. A mathematical model’s complexity computation requires performing the analyses over the run time based on data type employed with applicable methods. While providing the tools required to explore data complexities, AI and data analysis rely on foundational mathematical concepts, which can project novel perspectives, applicable solutions to challenges for uncertain elements to arise. Such intersections emerging on dynamic scales reveal the association between mathematics and data within constantly changing digitizing landscape where computer science, medicine, neuroscience, biology, engineering, mechanobiology, bioengineering, biomechanics, etc., are oriented towards the analysis, processing of models and data-centric prediction-based domains to name some.
To these ends, the aim of our symposium is to unify and implement the diverse approaches to complexity theory, mathematical sciences and applied complexity science for providing a key to understanding current complex problems so that mathematical frameworks can serve as plinths to understand the theories, AI’s role, algorithmic mechanisms and future science of complexity.
The potential topics include but are not limited to: