Accuracy and Efficiency in Scientific Machine Learning
IVADO and the听听are pleased to welcome some twenty international experts in the field of physics-informed machine learning who will share their latest advances in the mathematical analysis of methods using Neural Networks. Despite ongoing efforts to integrate machine learning techniques into computer simulations, significant challenges remain in developing accurate and efficient SciML methodologies for routine use in science and engineering applications.
The workshop aims to address these challenges by creating novel mathematical theories for error estimation and control. These developments are intended to improve the accuracy and efficiency of Neural Network approximations for initial- and boundary-value problems and operator learning.
During the workshop, students, postdocs, and researchers will have multiple opportunities for discussions with scientific leaders on these issues. A poster session and an informal cocktail reception are also on the agenda.
We encourage you, as well as your collaborators and students, to present your work during the poster session and to take this opportunity to engage in discussions with our guests and gain new perspectives.
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