106 citations · 136 across the 9 of their papers we have counts for
3 papers · 1 filter
Thermodynamically consistent physics-informed neural networks for hyperbolic systems
Ravi G. Patel, Indu Manickam, Nathaniel A. Trask +4
Physics-informed neural network architectures have emerged as a powerful tool for developing flexible PDE solvers which easily assimilate data, but face challenges related to the P…
A physics-informed operator regression framework for extracting data-driven continuum models
Ravi G. Patel, Nathaniel A. Trask, Mitchell A. Wood +1
The application of deep learning toward discovery of data-driven models requires careful application of inductive biases to obtain a description of physics which is both accurate a…
A block coordinate descent optimizer for classification problems exploiting convexity
Ravi G. Patel, Nathaniel A. Trask, Mamikon A. Gulian +1
Second-order optimizers hold intriguing potential for deep learning, but suffer from increased cost and sensitivity to the non-convexity of the loss surface as compared to gradient…