30 citations · 74 across the 6 of their papers we have counts for
3 papers · 1 filter
Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model
Wenqian Chen, Yucheng Fu, Panos Stinis
In this paper, we present a physics-informed neural network (PINN) approach for predicting the performance of an all-vanadium redox flow battery, with its physics constraints enfor…
Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics
Brian R. Bartoldson, Yeping Hu, Amar Saini +6
Data-driven modeling approaches can produce fast surrogates to study large-scale physics problems. Among them, graph neural networks (GNNs) that operate on mesh-based data are desi…
Latent Space Simulation for Carbon Capture Design Optimization
Brian Bartoldson, Rui Wang, Yucheng Fu +5
The CO2 capture efficiency in solvent-based carbon capture systems (CCSs) critically depends on the gas-solvent interfacial area (IA), making maximization of IA a foundational chal…