Computational Mechanics and Applied Mathematics: Perspectives from Young Scholars: Gimc Simai Young 2024
暫譯: 計算力學與應用數學:年輕學者的視角:Gimc Simai Young 2024
Marmo, Francesco, Cuomo, Salvatore, Cutolo, Arsenio
- 出版商: Springer
- 出版日期: 2025-03-28
- 售價: $9,910
- 貴賓價: 9.5 折 $9,415
- 語言: 英文
- 頁數: 256
- 裝訂: Quality Paper - also called trade paper
- ISBN: 3031765907
- ISBN-13: 9783031765902
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商品描述
This book collects the latest advances and innovations in the field of applied mathematics and computational mechanics, as presented at the 2nd Workshop GIMC SIMAI YOUNG, held in Naples, Italy, on July 10-12, 2024. The workshop was the joint effort of Computational Mechanics Group of the Italian Association of Theoretical and Applied Mechanics -AIMETA (GIMC) and Italian Society of Applied and Industrial Mathematics (SIMAI) and was meant to highlight the works of young researchers in the field. Topics include mathematical models for socio-epidemiological dynamics, efficient numerical methods for evolutionary PDEs, multi-scale approaches and machine learning techniques in material modelling, nonlinear material behaviour, computational methods for shells and spatial structures, assessment, monitoring, and design of masonry structures, particles in numerical simulations, non-Newtonian complex fluids, mathematical modelling in mechanobiology and oncology, mechanics of biological systems and bioinspired materials, computational approaches for complex dynamical systems, optimization methods for classical and data-driven approaches. The contributions, which were selected by means of a rigorous peer-review process, present a wealth of exciting ideas that will open novel research directions and foster multidisciplinary collaboration.
商品描述(中文翻譯)
本書收錄了應用數學和計算力學領域的最新進展和創新,這些內容是在2024年7月10日至12日於義大利那不勒斯舉行的第二屆GIMC SIMAI YOUNG研討會上發表的。該研討會是義大利理論與應用力學協會(AIMETA)計算力學小組(GIMC)和義大利應用與工業數學學會(SIMAI)共同舉辦的,旨在突顯年輕研究者在該領域的工作。主題包括社會流行病動態的數學模型、演化偏微分方程的高效數值方法、多尺度方法和材料建模中的機器學習技術、非線性材料行為、殼體和空間結構的計算方法、砌體結構的評估、監測和設計、數值模擬中的粒子、非牛頓複雜流體、機械生物學和腫瘤學中的數學建模、生物系統和生物啟發材料的力學、複雜動態系統的計算方法、以及經典和數據驅動方法的優化技術。這些貢獻經過嚴格的同行評審過程選出,呈現出豐富的創新想法,將開啟新的研究方向並促進多學科合作。