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Data-driven shape inference in three-dimensional steady-state supersonic flows: Optimizing a discrete loss with JAX-Fluids

Aaron B. Buhendwa1,*, Deniz A. Bezgin1,†, Petr Karnakov2,‡, Nikolaus A. Adams1,3,§, and Petros Koumoutsakos2,∥

  • 1Technical University of Munich, School of Engineering and Design, Chair of Aerodynamics and Fluid Mechanics, Boltzmannstraße 15, 85748 Garching bei München, Germany
  • 2Computational Science and Engineering Laboratory, Harvard University, Cambridge, Massachusetts 02138, USA
  • 3Technical University of Munich, Munich Institute of Integrated Materials, Energy and Process Engineering, Lichtenbergstraße 4a, 85748 Garching bei München, Germany

  • *Contact author: aaron.buhendwa@tum.de
  • †Contact author: deniz.bezgin@tum.de
  • ‡Contact author: pkarnakov@gmail.com
  • §Contact author: nikolaus.adams@tum.de
  • ∥Contact author: petros@seas.harvard.edu

JANC: A cost-effective, differentiable compressible reacting flow solver featured with JAX-based adaptive mesh refinement

Haocheng Wen, Faxuan Luo, Sheng Xu, and Bing Wang

Computer Physics Communications 319 109915 (2026)

Bayesian inference for PDE-based inverse problems using the optimization of a discrete loss

Lucas Amoudruz, Sergey Litvinov, Costas Papadimitriou, and Petros Koumoutsakos

Computer Methods in Applied Mechanics and Engineering 455 118903 (2026)

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