Data-driven physics-informed neural networks: A digital twin perspective

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Maziar Raissi, Paris Perdikaris, George Em Karniadakis · Journal of Computational Physics · 2018 · 16.9K citations · Full text

Natural Sciences, Computer Science, Physics +16

High-Re solutions for incompressible flow using the Navier-Stokes equations and a multigrid method

Urmila Ghia, K. N. Ghia, C. T. Shin · Journal of Computational Physics · 1982 · 4.2K citations

Natural Sciences, Mechanical Engineering, Physics +17

Digital Twin in Industry: State-of-the-Art

Fei Tao, He Zhang, Ang Liu et al. · IEEE Transactions on Industrial Informatics · 2019 · 3.8K citations

Engineering, Industrial Engineering, Manufacturing Engineering +17

Automatic differentiation in machine learning: a survey

Atılım Güneş Baydin, Barak A. Pearlmutter, Alexey Radul et al. · Maynooth University ePrints and eTheses Archive (Maynooth University) · 2015 · 2.1K citations · Full text

Computing, Computer Science, Derivatives +10

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Adil Rasheed, Omer San, Trond Kvamsdal · IEEE Access · 2020 · 1.7K citations · Full text

Engineering, Computer Science, Systems Engineering +15