Center for Computational Fluid Dynamics

Data Management, Model Reduction, Deep Neural Nets

  • H. Antil, R. Löhner and R. Price – Data Assimilation With Deep Neural Nets Informed by Nudging; chapter in Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators (G. Rozza, G. Stabile, M. Gunzburger and M. D’Elia eds.), Lecture Notes in Computational Science and Engineering, vol 151, Springer, Cham. (2024). https://doi.org/10.1007/978-3-031-55060-7_2
  • R. Löhner and H. Antil – Neural Network Representation of Time Integrators; Int. J. Num. Meth. Eng. 124(18), 4192-4198 (2023). https://doi.org/10.1002/nme.7306
  • T.S. Brown, H. Antil, R. Löhner, D. Verma and F. Togashi – Parallel Deep ResNets for Chemically Reacting Flows; AIAA-2022-1076 (2022). https://doi.org/10.2514/6.2022-1076
  • H. Antil, T.S. Brown, R. Löhner, F. Togashi and D. Veerma – Deep Neural Nets with Fixed Bias Configuration; arXiv:2107.01308 [math.OC] (2021).
  • T. Brown, H. Antil, R. Löhner, F. Togashi and D. Verma – Novel DNNS for Stiff ODEs With Applications to Chemically Reacting Flows; {\sl CFDML2021: 2nd Int.\ Workshop on the Application of Machine Learning Techniques to Computational Fluid Dynamics and Solid Mechanics Simulations and Analysis }, Held in conjunction with the International Supercomputing Conference (ISC) High Performance 2021, July 2 (2021). arXiv:2104.01914v1 [cs.LG] (2021).
  • R. Löhner, H. Antil, H.R. Tamaddon-Jahromi, N.K. Chakshu and P. Nithiarasu – Deep Learning or Interpolation for Inverse Modelling of Heat and Fluid Flow Problems ?; Int. J. Num. Meth. Heat and Fluid Flow (2020) https://doi.org/10.1108/HFF-11-2020-0684.
  • H. Antil, R. Khatri, R. Löhner and D. Verma – Fractional Deep Neural Network Via Constrained Optimization; arXiv:2004.00719 math.OC. Mach. Learn.: Sci. Technol. 2, 1 015003 https://dx.doi.org/10.1088/2632-2153/aba8e7