150 citations · 271 across the 7 of their papers we have counts for
4 papers · 1 filter
Deep smoothness WENO scheme for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators
Tatiana Kossaczká, Ameya D. Jagtap, Matthias Ehrhardt
In this paper, we introduce an improved version of the fifth-order weighted essentially non-oscillatory (WENO) shock-capturing scheme by incorporating deep learning techniques. The…
Error estimates for physics informed neural networks approximating the Navier-Stokes equations
Tim De Ryck, Ameya D. Jagtap, Siddhartha Mishra
We prove rigorous bounds on the errors resulting from the approximation of the incompressible Navier-Stokes equations with (extended) physics informed neural networks. We show that…
Physics-informed neural networks for inverse problems in supersonic flows
Ameya D. Jagtap, Zhiping Mao, Nikolaus Adams +1
Accurate solutions to inverse supersonic compressible flow problems are often required for designing specialized aerospace vehicles. In particular, we consider the problem where we…
Method of Relaxed Streamline Upwinding for Hyperbolic Conservation Laws
Ameya D. Jagtap
In this work a new finite element based Method of Relaxed Streamline Upwinding is proposed to solve hyperbolic conservation laws. Formulation of the proposed scheme is based on rel…