72 citations · 164 across the 18 of their papers we have counts for
3 papers · 1 filter
Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems
Alexander Heinlein, Amanda A. Howard, Damien Beecroft +1
Multiscale problems are challenging for neural network-based discretizations of differential equations, such as physics-informed neural networks (PINNs). This can be (partly) attri…
A multifidelity approach to continual learning for physical systems
Amanda Howard, Yucheng Fu, Panos Stinis
We introduce a novel continual learning method based on multifidelity deep neural networks. This method learns the correlation between the output of previously trained models and t…
Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems
Amanda A. Howard, Mauro Perego, George E. Karniadakis +1
Operator learning for complex nonlinear systems is increasingly common in modeling multi-physics and multi-scale systems. However, training such high-dimensional operators requires…