1.4k citations · 1.4k across the 9 of their papers we have counts for
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Deep LPPLS: Forecasting of temporal critical points in natural, engineering and financial systems
Joshua Nielsen, Didier Sornette, Maziar Raissi
The Log-Periodic Power Law Singularity (LPPLS) model offers a general framework for capturing dynamics and predicting transition points in diverse natural and social systems. In th…
PINNs-TF2: Fast and User-Friendly Physics-Informed Neural Networks in TensorFlow V2
Reza Akbarian Bafghi, Maziar Raissi
Physics-informed neural networks (PINNs) have gained prominence for their capability to tackle supervised learning tasks that conform to physical laws, notably nonlinear partial di…
Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data
Maziar Raissi, Alireza Yazdani, George Em Karniadakis
We present hidden fluid mechanics (HFM), a physics informed deep learning framework capable of encoding an important class of physical laws governing fluid motions, namely the Navi…