Inferring incompressible two-phase flow fields from the interface motion using physics-informed neural networks. - Aaron B. Buhendwa; Stefan Adami; Nikolaus A. Adams (CoRR, 2021)

A fully-differentiable compressible high-order computational fluid dynamics solver. - Deniz A. Bezgin; Aaron B. Buhendwa; Nikolaus A. Adams (CoRR, 2021)

Consistent and symmetry preserving data-driven interface reconstruction for the level-set method. - Aaron B. Buhendwa; Deniz A. Bezgin; Nikolaus A. Adams (J. Comput. Phys., 2022)

JAX-Fluids: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows. - Deniz A. Bezgin; Aaron B. Buhendwa; Nikolaus A. Adams (Comput. Phys. Commun., 2023)

Rational-WENO: A lightweight, physically-consistent three-point weighted essentially non-oscillatory scheme. - Shantanu Shahane; Sheide Chammas; Deniz A. Bezgin; Aaron B. Buhendwa; Steffen J. Schmidt; Nikolaus A. Adams; Spencer H. Bryngelson; Yi-Fan Chen; Qing Wang 032; Fei Sha; Leonardo Zepeda-Núñez (CoRR, 2024)

JAX-Fluids 2.0: Towards HPC for differentiable CFD of compressible two-phase flows. - Deniz A. Bezgin; Aaron B. Buhendwa; Nikolaus A. Adams (Comput. Phys. Commun., 2025)

ML-ILES: End-to-end optimization of data-driven high-order Godunov-type finite-volume schemes for compressible homogeneous isotropic turbulence. - Deniz A. Bezgin; Aaron B. Buhendwa; Steffen J. Schmidt; Nikolaus A. Adams (J. Comput. Phys., 2025)