86 citations · 174 across the 29 of their papers we have counts for
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Maximum-likelihood Estimators in Physics-Informed Neural Networks for High-dimensional Inverse Problems
Gabriel S. Gusmão, Andrew J. Medford
Physics-informed neural networks (PINNs) have proven a suitable mathematical scaffold for solving inverse ordinary (ODE) and partial differential equations (PDE). Typical inverse P…
A Priori Calibration of Transient Kinetics Data via Machine Learning
M. Ross Kunz, Adam Yonge, Rakesh Batchu +5
The temporal analysis of products reactor provides a vast amount of transient kinetic information that may be used to describe a variety of chemical features including the residenc…
Kinetics-Informed Neural Networks
Gabriel S. Gusmão, Adhika P. Retnanto, Shashwati C. da Cunha +1
Chemical kinetics and reaction engineering consists of the phenomenological framework for the disentanglement of reaction mechanisms, optimization of reaction performance and the r…