Physics-Informed CNNs for Super-Resolution of Sparse Observations on
Dynamical Systems

Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems

31 October 2022

Luca Magri

Papers citing "Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems"

8 / 8 papers shown

Title
How to Re-enable PDE Loss for Physical Systems Modeling Under Partial Observation Haodong Feng Yue Wang Dixia Fan AI4CE 75 0 0 12 Dec 2024
Reconstructing unsteady flows from sparse, noisy measurements with a physics-constrained convolutional neural network Yaxin Mo Luca Magri AI4CE 28 0 0 30 Aug 2024
Thermodynamics-informed super-resolution of scarce temporal dynamics data Carlos Bermejo-Barbanoj B. Moya Alberto Badías Francisco Chinesta Elías Cueto AI4CE 21 2 0 27 Feb 2024
Interpretable structural model error discovery from sparse assimilation increments using spectral bias-reduced neural networks: A quasi-geostrophic turbulence test case R. Mojgani A. Chattopadhyay P. Hassanzadeh 33 7 0 22 Sep 2023
Learning the solution operator of two-dimensional incompressible Navier-Stokes equations using physics-aware convolutional neural networks Viktor Grimm Alexander Heinlein A. Klawonn AI4CE 19 4 0 04 Aug 2023
Physics-Informed Computer Vision: A Review and Perspectives C. Banerjee Kien Nguyen Clinton Fookes G. Karniadakis PINN AI4CE 34 28 0 29 May 2023
Reconstruction, forecasting, and stability of chaotic dynamics from partial data Elise Özalp G. Margazoglou Luca Magri AI4TS 13 10 0 24 May 2023
Lagrangian Neural Networks M. Cranmer S. Greydanus Stephan Hoyer Peter W. Battaglia D. Spergel S. Ho PINN 130 424 0 10 Mar 2020