El Instituto de Ingeniería Matemática y Computacional (IMC) los saluda atentamente y los invita al seminario que se dictará esta semana.
Paul Escapul-Inchauspé, Data Observatory.
Miércoles 31 de mayo de 2023, 13 hrs. (Presencial en Auditorio Edificio San Agustín; Link Zoom disponible escribiendo a Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.)
ABSTRACT
Over the last 8 years, the usage of AI-based solutions such as deep learning in mathematical and computational engineering has experienced considerable growth in popularity, providing practitioners with new opportunities and approaches for performing simulations.
In this presentation, we analyze the data-centric paradigm outlined by Zha et al., with an emphasis on its implications for research and industry. We also consider what effects it may have on the fields of mathematical and computational engineering, along with the new prospects and trends it may create.
In particular, we present physics-informed machine learning (PIML) as a novel framework in computational mathematics and engineering. PIML couples observations with domain/physics knowledge in a single system, proving to be efficient for multi-physics, high-dimensional, and noisy problems. In particular, we explored physics-informed neural networks (PINNs) in greater detail.
We discuss the practical implementation of existing approaches, their strengths and limitations, and comment on current trends and future research opportunities in these areas.
