Date:
Speaker : Yue Yu, Lehigh University
Time : 16:00 - 17:00 CEST (Rome/Paris)
Hosted at: SISSA, International School of Advanced Studies, Trieste, Italy
Zoom : A link will appear here
Organizers : Pavan Pranjivan Mehta* (pavan.mehta@sissa.it) and Arran Fernandez** (arran.fernandez@emu.edu.tr)
* SISSA, International School of Advanced Studies, Italy
** Eastern Mediterranean University, Northern Cyprus
Keywords: Scientific Machine Learning, Peridynamics, Nonlocal Kernel Regression
Abstract: Nonlocal models, including peridynamics and fractional PDEs, often use integral operators that embed length-scales in their definition. However, the integrands especially the kernels in these operators are difficult to define from the data that are typically available for a given physical system, such as laboratory mechanical property tests.
In this talk, we will consider learning of complex material responses as an exemplar problem to investigate automated nonlocal model discovery from experimental data. In particular, we propose to parameterize the mapping between excitation and corresponding system responses in the form of nonlocal operators, and infer the integral kernels from experimental measurements. As such, the model is built as mappings between infinite-dimensional function spaces, and the learnt models are resolution-agnostic. Moreover, the nonlocal operator architecture also allows the incorporation of fundamental mathematical and physics knowledge. Both properties improve the learning efficacy and robustness from scarce measurements.
To demonstrate the applicability of our nonlocal operator learning framework, two typical scenarios will be discussed: (1) learning of a material-specific constitutive law, and (2) development of a foundation constitutive law across multiple materials.
Biography: Yue Yu received her B.S. from Peking University in 2008, and her Ph.D. from Brown University in 2014. She was a postdoc fellow at Harvard University after graduation, and then she joined Lehigh University as an assistant professor of applied mathematics and was promoted to full professor in 2023. Her research lies in the area of numerical analysis and scientific computing, with recent projects focusing on nonlocal problems and scientific machine learning. She has received an NSF Early Career award and an AFOSR Young Investigator Program (YIP) award.
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