48 citations · 52 across the 11 of their papers we have counts for
3 papers · 1 filter
Tethering Broken Themes: Aligning Neural Topic Models with Labels and Authors
Mayank Nagda, Phil Ostheimer, Sophie Fellenz
Topic models are a popular approach for extracting semantic information from large document collections. However, recent studies suggest that the topics generated by these models o…
SetPINNs: Set-based Physics-informed Neural Networks
Mayank Nagda, Phil Ostheimer, Thomas Specht +5
Physics-Informed Neural Networks (PINNs) solve partial differential equations using deep learning. However, conventional PINNs perform pointwise predictions that neglect dependenci…
HANNA: Hard-constraint Neural Network for Consistent Activity Coefficient Prediction
Thomas Specht, Mayank Nagda, Sophie Fellenz +3
We present the first hard-constraint neural network for predicting activity coefficients (HANNA), a thermodynamic mixture property that is the basis for many applications in scienc…