Stress state and waves in the lithospheric plate simulation: A 3rd generation AI architecture

Abstract

Natural disasters present ongoing risks to human life and the global economy, with climate change and environmental factors exacerbating these threats. This article introduces an innovative approach to earthquake prediction and modeling, utilizing a combination of modern mathematical techniques, data-driven methodologies, and artificial intelligence (AI). By integrating the finite element method and wavelet collocation method, our approach enables the solution of complex partial differential equations involving fractional derivatives. This integration provides a more accurate prediction of earthquake behavior by leveraging AI analysis of the resulting numerical solutions.Our advanced modeling technique enables three-dimensional earthquake modeling, surpassing the limitations of traditional methods and offering unprecedented insights. The comprehensive AI approach shows great potential for improving our understanding of earthquake behavior, facilitating the design of earthquake-resistant structures, and effectively mitigating the devastating impacts of earthquakes.This research significantly contributes to the field of disaster risk reduction and offers practical applications for both researchers and practitioners. By combining mathematical rigor, data-driven insights, and the power of AI, our next-generation approach opens new avenues for enhancing our preparedness and response to seismic events.

Keywords

• Stress state
• Finite element method
• Wavelet collocation method
• Partial differential equations
• Natural disasters
• Numerical simulation