72 citations · 164 across the 18 of their papers we have counts for
4 papers · 1 filter
Physics-Guided Continual Learning for Predicting Emerging Aqueous Organic Redox Flow Battery Material Performance
Yucheng Fu, Amanda Howard, Chao Zeng +3
Aqueous organic redox flow batteries (AORFBs) have gained popularity in renewable energy storage due to their low cost, environmental friendliness and scalability. The rapid discov…
Stacked networks improve physics-informed training: applications to neural networks and deep operator networks
Amanda A Howard, Sarah H Murphy, Shady E Ahmed +1
Physics-informed neural networks and operator networks have shown promise for effectively solving equations modeling physical systems. However, these networks can be difficult or i…
Efficient kernel surrogates for neural network-based regression
Saad Qadeer, Andrew Engel, Amanda Howard +4
Despite their immense promise in performing a variety of learning tasks, a theoretical understanding of the limitations of Deep Neural Networks (DNNs) has so far eluded practitione…
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…