Physics-informed neural networks for inverse problems in supersonic flows

Accurate solutions to inverse supersonic compressible flow problems are often required for designing specialized aerospace vehicles. In particular, we consider the problem where we have data available for density gradients from Schlieren photography as well as data at the inflow and part of wall boundaries. These inverse problems are notoriously difficult and traditional methods may not be adequate to solve such ill-posed inverse problems. To this end, we employ the physics-informed neural networks (PINNs) and its extended version, extended PINNs (XPINNs), where domain decomposition allows deploying locally powerful neural networks in each subdomain, which can provide additional expressivity in subdomains,…

• AV
  Alexander von Humboldt-Stiftung
• BU
  Brown University
• TU
  Technische Universität München
• OO
  Office of the Secretary of Defense
• MU
  Multidisciplinary University Research Initiative

  Award: FA9550-20-1-0358
• AF
  Air Force Office of Scientific Research

  Awards: FA9550-, FA9550-20-1-0358, FA9550-20-1, FA9550-20, FA9550, FA9550- 20-1-0358, 1-0358