As the demand for data-driven solutions and complex problem-solving continues to escalate, the synergy between scientific computing and machine learning becomes increasingly crucial. The SCML seminar series serves several key objectives:
As the demand for data-driven solutions and complex problem-solving continues to escalate, the synergy between scientific computing and machine learning becomes increasingly crucial. The SCML seminar series serves several key objectives:
As the demand for data-driven solutions and complex problem-solving continues to grow, the synergy between scientific computing and machine learning is becoming increasingly crucial. In this seminar, we will delve into state-of-the-art computational techniques, innovative machine learning algorithms, and their combined potential to revolutionize research across various domains.
With the rapid developments in technology, computational and applied mathematics has played an important and unprecedented role in many scientific disciplines.
I will give an elementary introduction of basic deep learning models and training algorithms from a mathematical viewpoint. In particular, I will relate some basic deep learning models with finite element and multigrid methods.