most citedPhysics-Informed Neural Network based inverse framework for time-fractional differential equations for rheology

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

cs.NE2024★ 1 cited

Physics-Informed Neural Network based inverse framework for time-fractional differential equations for rheology

Sukirt Thakur, Harsa Mitra, Arezoo M. Ardekani

Time-fractional differential equations offer a robust framework for capturing intricate phenomena characterized by memory effects, particularly in fields like biotransport and rheo…

physics.flu-dyn2023

Temporal Consistency Loss for Physics-Informed Neural Networks

Sukirt Thakur, Maziar Raissi, Harsa Mitra +1

Physics-informed neural networks (PINNs) have been widely used to solve partial differential equations in a forward and inverse manner using deep neural networks. However, training…