numeric simulations

Towards trustworthy use of scientific machine-learning in large scale numerical simulations

Alena Kopanicakova, Researcher, Brown University

May 13, 09:00 - 10:00

B9 L3 R3128

numeric simulations

Recently, scientific machine learning (SciML) has expanded the capabilities of traditional numerical approaches by simplifying computational modeling and providing cost-effective surrogates. However, SciML models suffer from the absence of explicit error control, a computationally intensive training phase, and a lack of reliability in practice. In this talk, we will take the first steps toward addressing these challenges by exploring two different research directions.

Jorge Alejandro Amador Herrera

Ph.D. Student, Applied Mathematics and Computational Science

numeric simulations scientific computing visual computing machine learning quantum algorithms

Research Interest His research interests include scientific and visual computing, numerical simulations, machine learning, and the modelling of natural phenomena. He works on physically-based simulations. Honors and Awards During his studies at UNAM, Alejandro has been awarded many times. In 2013, he received the UNAM Scholarship from the university. He was also awarded the Telmex Foundation Scholarship from 2013 to 2016, and the CONACYT Fellowship from 2017 to 2018. He graduated with summa cum laude in 2018. Alejandro has also received multiple awards from his participation in academic