Applied Mathematics of Complexity, Nonlocality, and Uncertainty in Extreme Material Response

This meeting connects difficult questions in material response under extreme conditions with mathematical tools for nonlocality, complexity, uncertainty, and multiscale behavior. Themes include nonlocal PDE and integro-differential models, peridynamics, phase-field and variational methods, uncertainty quantification, numerical analysis, and structure-preserving scientific machine learning.

Organizing Team

• Robert P. Lipton, PI / scientific lead
• Scott J. Baldridge, Co-PI / TIAMS Director
• Kaushik Dayal, senior personnel / external scientific organizer

TIAMS Emphasis

Scientific machine learning is treated as part of a modeling pipeline that must respect physical structure, stability, conservation, thermodynamics, uncertainty, and mathematical validation.