Zenith HPDaSc seminar, Monday 7 November 2022, 10h15-11h45
BAT5-01.124, Campus Saint Priest
Introduction and welcome
Patrick Valduriez
Dbfication or Dbfiction in Deep Learning Activities
Marta Mattoso
COPPE/UFRJ, Rio de Janeiro, Brazil
Database management techniques have a lot to contribute to generating and selecting a deep learning model. In this talk, we present current initiatives of the database community towards using data management techniques to improve deep learning activities, to discuss pros and cons. Then, we will focus on provenance-based user steering to evaluate different neural network execution configurations. Provenance data adds semantics to the metrics of each configuration, which can help humans in evaluating and reproducing the models proposed by automatic tools.
An introduction to physics-informed neural networks
Alvaro Coutinho
COPPE/UFRJ, Rio de Janeiro, Brazil
In this talk we will give a brief introduction to Physics-Informed Neural Networks (PINN), that are neural networks that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself. PINNs are nowadays used to solve PDEs, fractional equations, integral-differential equations, and stochastic problems. PINN fits observed data while reducing a PDE residual. We will discuss the basic math and algorithmic steps and show some current advanced applications.
