466 citations · 1.7k across the 54 of their papers we have counts for
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Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery
QiZhi He, Yucheng Fu, Panos Stinis +1
Numerical modeling and simulation have become indispensable tools for advancing a comprehensive understanding of the underlying mechanisms and cost-effective process optimization a…
Physics-informed CoKriging model of a redox flow battery
Amanda A. Howard, Alexandre M. Tartakovsky
Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently, however, there is a need for fast and accurate models of the charge-disch…
Physics-constrained deep neural network method for estimating parameters in a redox flow battery
QiZhi He, Panos Stinis, Alexandre Tartakovsky
In this paper, we present a physics-constrained deep neural network (PCDNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium redox flow battery (V…
A New Approach For Learning Coarse-Grained Potentials with Application to Immiscible Fluids
Peiyuan Gao, Xiu Yang, Alexandre M. Tartakovsky
Even though atomistic and coarse-grained (CG) models have been used to simulate liquid nanodroplets in vapor, very few rigorous studies of the liquid-liquid interface structure are…
MARTINI-based Coarse-grained Model for Poly(alpha-peptoid)s
Peiyuan Gao, Alex Tartakovsky
In this paper, we present a new coarse-grained (CG) model for poly (alpha-peptoid)s that is compatible with the MARTINI CG FF. In the proposed model, CG poly (alpha-peptoid) is com…