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

Document Type

Article

Source of Publication

Results in Physics

Publication Date

10-1-2023

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.

ISSN

2211-3797

Publisher

Elsevier BV

Volume

53

Disciplines

Life Sciences | Physical Sciences and Mathematics

Keywords

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

Scopus ID

85171330624

Indexed in Scopus

yes

Open Access

no

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