Learning characteristic parameters and dynamics of centrifugal pumps under multi-phase flow using physics-informed neural networks

no code implementations • 4 Oct 2023 • Felipe de Castro Teixeira Carvalho, Kamaljyoti Nath, Alberto Luiz Serpa, George Em Karniadakis

In this paper, we formulate a machine learning model based on Physics-Informed Neural Networks (PINNs) to estimate crucial system parameters.

Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines

no code implementations • 26 Apr 2023 • Kamaljyoti Nath, Xuhui Meng, Daniel J Smith, George Em Karniadakis

In other words, the mean value model uses both the PINN model and the DNNs to represent the engine's states, with the PINN providing a physics-based understanding of the engine's overall dynamics and the DNNs offering a more engine-specific and adaptive representation of the empirical formulae.

Learning via Long Short-Term Memory (LSTM) network for predicting strains in Railway Bridge members under train induced vibration

no code implementations • 8 Nov 2021 • Amartya Dutta, Kamaljyoti Nath

Actual field data has been used for the purpose of predicting strain in different members using strain data from a single member, yet it has been observed that they are quite agreeable to those of ground truth values.