A framework based on symbolic regression coupled with eXtended Physics-Informed Neural Networks for gray-box learning of equations of motion from data

2023/07/25 by Elham Kiyani, Khemraj Shukla, George Em Karniadakis +1 · 2 citations
Physics and Astronomy · Engineering · #Model Reduction and Neural Networks #Fluid Dynamics and Turbulent Flows #Lattice Boltzmann Simulation Studies

paper · doi:10.1016/j.cma.2023.116258

Citations

• Understanding deep learning (still) requires rethinking generalization
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems
• When and why PINNs fail to train: A neural tangent kernel perspective

Cited by

• Turbulence Closure in RANS and Flow Inference around a Cylinder using PINNs and Sparse Experimental Data
• Discovering equations from data: symbolic regression in dynamical systems