1 citations · 1 across the 3 of their papers we have counts for
3 papers
physics.flu-dyn2025
ELPINN: Eulerian Lagrangian Physics-Informed Neural Network
Sukirt Thakur, Maziar Raissi
Physics-Informed Neural Networks (PINNs) have gained widespread popularity for solving inverse and forward problems across a range of scientific and engineering domains. However, m…
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…