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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

Structural identifiability of constitutive relations from indirect field observations

Y Sungtaek Ju and D L Hartmann

PNAS Nexus 5 pgag281 (2026)

Neural inference of fluid-structure interactions from sparse off-body measurements

Rui Tang, Ke Zhou, Jifu Tan, and Samuel J. Grauer

Phys. Rev. Fluids 11 074901 (2026)

Ultrasonic-Net: a physics-informed neural network for ultrasonic field prediction in cleaning systems

Yu Jiao, Alexander Bußmann, Steffen J. Schmidt, Marcus Giglmaier, and Nikolaus A. Adams

Engineering Applications of Computational Fluid Mechanics 20 2690336 (2026)

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)

Comparative Study of Physics-Informed Neural Networks (PINNs) and Optimizing a Discrete Loss (ODIL) for Forward and Inverse Problems in Unsteady Non-Linear Partial Differential Equations

Vasco L. Carvalho and José C. F. Pereira

Computation 14 172 (2026)

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