Publication Categories
- H. Antil, R. Löhner and R. Price – NINNs: Nudging Induced Neural Networks; https://doi.org/10.48550/arXiv.2203.07947
- H. Antil, T.S. Brown, R. Löhner, F. Togashi and D. Veerma – Deep Neural Nets with Fixed Bias Configuration; Numerical Algebra, Control and Optimization https://doi.org/10.3934/naco.2022016 (2022). doi: 10.3934/naco.2022016
- 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, R. Löhner and R. Price – Data Assimilation With Deep Neural Nets Informed by Nudging; (2021). https://arxiv.org/pdf/2111.11505.pdf
- 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