Data-driven prediction in dynamical systems: recent developments

Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2022 · 108 citations

• Computer Science
• Artificial Intelligence
• Data science

In recent years, we have witnessed a significant shift toward ever-more complex and ever-larger-scale systems in the majority of the grand societal challenges tackled in applied sciences. The need to comprehend and predict the dynamics of complex systems have spurred developments in large-scale simulations and a multitude of methods across several disciplines. The goals of understanding and prediction in complex dynamical systems, however, have been hindered by high dimensionality, complexity and chaotic behaviours. Recent advances in data-driven techniques and machine-learning approaches have revolutionized how we model and analyse complex systems. The integration of these techniques with dynamical systems theory opens up opportunities to tackle previously unattainable challenges in modelling and prediction of dynamical systems. While data-driven prediction methods have made great strides in recent years, it is still necessary to develop new techniques to improve their applicability to a wider range of complex systems in science and engineering. This focus issue shares recent developments in the field of complex dynamical systems with emphasis on data-driven, data-assisted and artificial intelligence-based discovery of dynamical systems. This article is part of the theme issue 'Data-driven prediction in dynamical systems'.

Mathematical model of the feedback between global supply chain disruption and COVID-19 dynamics

Scientific Reports · 2021 · 45 citations

• Computer Science
• Business
• Risk analysis (engineering)

The pandemic of COVID-19 has become one of the greatest threats to human health, causing severe disruptions in the global supply chain, and compromising health care delivery worldwide. Although government authorities sought to contain the spread of SARS-CoV-2, by restricting travel and in-person activities, failure to deploy time-sensitive strategies in ramping-up of critical resource production exacerbated the outbreak. Here, we developed a mathematical model to analyze the effects of the interaction between supply chain disruption and infectious disease dynamics using coupled production and disease networks built on global data. Analysis of the supply chain model suggests that time-sensitive containment strategies could be created to balance objectives in pandemic control and economic losses, leading to a spatiotemporal separation of infection peaks that alleviates the societal impact of the disease. A lean resource allocation strategy can reduce the impact of supply chain shortages from 11.91 to 1.11% in North America. Our model highlights the importance of cross-sectoral coordination and region-wise collaboration to optimally contain a pandemic and provides a framework that could advance the containment and model-based decision making for future pandemics.

Data-Driven Forecasting of Postflutter Responses of Geometrically Nonlinear Wings

AIAA Journal · 2020 · 39 citations

• Computer Science
• Computer Science
• Engineering

Ensuring adequate flutter margins is a critical step in aircraft design. However, in the presence of nonlinear effects, subcritical limit-cycle oscillations can arise even before flutter occurs. When nonlinear effects are anticipated, postflutter analyses have to be integrated into aircraft design in addition to flutter computations for preventing this undesirable subcritical behavior. Therefore, there is a need for computationally fast postflutter analysis methods suitable for design applications. This paper investigates a data-driven method to forecast the bifurcation behavior of a geometrically nonlinear wing, demonstrating its suitability to analyze realistic nonlinear aeroelastic systems. The method efficiently forecasts postflutter responses using only a few system transient computations in the preflutter regime. First, the accuracy of forecasted bifurcation diagrams is verified against time-marching results. Next, wing design parameters are varied to investigate their impact on postflutter responses and show the method suitability to parametric studies.