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Scientific machine learning through physics–informed neural networks: Where we are and what's next
Abstract Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode
model equations, like Partial Differential Equations (PDE), as a component of the neural …
model equations, like Partial Differential Equations (PDE), as a component of the neural …
Physics-guided, physics-informed, and physics-encoded neural networks in scientific computing
SA Faroughi, N Pawar, C Fernandes, M Raissi… - ar**s between function spaces and offer a new
simulation paradigm for real-time prediction of complex dynamics for realistic diverse …
simulation paradigm for real-time prediction of complex dynamics for realistic diverse …
Model reduction and neural networks for parametric PDEs
We develop a general framework for data-driven approximation of input-output maps
between infinitedimensional spaces. The proposed approach is motivated by the recent …
between infinitedimensional spaces. The proposed approach is motivated by the recent …
Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness
Full waveform inversion (FWI) infers the subsurface structure information from seismic
waveform data by solving a non-convex optimization problem. Data-driven FWI has been …
waveform data by solving a non-convex optimization problem. Data-driven FWI has been …