no code implementations • 20 Feb 2023 • Jacob H. Seidman, Georgios Kissas, George J. Pappas, Paris Perdikaris
Unsupervised learning with functional data is an emerging paradigm of machine learning research with applications to computer vision, climate modeling and physical systems.
1 code implementation • 3 Oct 2022 • Sifan Wang, Hanwen Wang, Jacob H. Seidman, Paris Perdikaris
Continuous neural representations have recently emerged as a powerful and flexible alternative to classical discretized representations of signals.
no code implementations • 7 Jun 2022 • Jacob H. Seidman, Georgios Kissas, Paris Perdikaris, George J. Pappas
Supervised learning in function spaces is an emerging area of machine learning research with applications to the prediction of complex physical systems such as fluid flows, solid mechanics, and climate modeling.
no code implementations • L4DC 2020 • Jacob H. Seidman, Mahyar Fazlyab, Victor M. Preciado, George J. Pappas
By interpreting the min-max problem as an optimal control problem, it has recently been shown that one can exploit the compositional structure of neural networks in the optimization problem to improve the training time significantly.