Comparison between numerical simulations and experimental data is essential for the validation of fracture models to gain confidence in their predictability and reliability. Recently, new approaches for fracture modeling, such as phase-field fracture modeling, peridynamics, the eigenerosion approach, or the reproducing kernel particle method, have shown promising results for simulating complex fracture phenomena. However, the validation of these models cannot be carried out without the design of suitable validation experiments. Moreover, while Scientific Machine Learning and Uncertainty Quantification have been successfully applied in many fields, their application in fracture mechanics is still limited. This workshop aims at bringing together experts in experimental and computational fracture mechanics, scientific machine learning, and uncertainty quantification to discuss the state of the art in experimental design and computational modeling for fracture mechanics. Specific objectives will be to find new opportunities for using methods from scientific machine learning and uncertainty quantification in the context of fracture mechanics, and identify challenges and pathways for robust validation of fracture models through integration of experimental and modeling efforts.

Organizing Committee

Patrick Diehl, Louisiana State University
Serge Prudhomme, Polytechnique Montréal
Pablo Seleson, Oak Ridge National Laboratory
Gowri Srinivasan, Los Alamos National Laboratory

Early-Career Assistant

Noujoud Nader, Louisiana State University

Scientific Committee

Robert Lipton, Louisiana State University
K. Ravi-Chandar, The University of Texas at Austin
Stewart Silling, Sandia National Laboratories
Yuri Bazilevs, Brown University
Thomas Wick, Leibniz Universität Hannover
Anna Pandolfi, Politecnico di Milano
J.S. Chen, University of California, San Diego
Marta D’Elia, Pasteur Labs and Stanford University
John Dolbow, Duke University
Steve Brunton, University of Washington
Mayank Tyagi, Louisiana State University

Logistics

Karen Jones, Louisiana State University
Jennifer Fontenot, Louisiana State University
Bethany Roicki, USACM

For questions, please contact admin@usacm.org.