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20212025
most citedPhysics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines

39 citations · 95 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.LG2025

AMORE: Adaptive Multi-Output Operator Network for Stiff Chemical Kinetics

Kamaljyoti Nath, Additi Pandey, Bryan T. Susi +2

Time integration of stiff systems is a primary source of computational cost in combustion, hypersonics, and other reactive transport systems. This stiffness can introduce time scal…

cs.LG2025★ 1 cited

Leveraging Deep Operator Networks (DeepONet) for Acoustic Full Waveform Inversion (FWI)

Kamaljyoti Nath, Khemraj Shukla, Victor C. Tsai +6

Full Waveform Inversion (FWI) is an important geophysical technique considered in subsurface property prediction. It solves the inverse problem of predicting high-resolution Earth…

cs.LG2024★ 10 cited

A Digital Twin for Diesel Engines: Operator-infused Physics-Informed Neural Networks with Transfer Learning for Engine Health Monitoring

Kamaljyoti Nath, Varun Kumar, Daniel J. Smith +1

Improving diesel engine efficiency, reducing emissions, and enabling robust health monitoring have been critical research topics in engine modelling. While recent advancements in t…

cs.LG2023★ 18 cited

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

Felipe de Castro Teixeira Carvalho, Kamaljyoti Nath, Alberto Luiz Serpa +1

Electrical submersible pumps (ESPs) are prevalently utilized as artificial lift systems in the oil and gas industry. These pumps frequently encounter multiphase flows comprising a…

cs.LG2023★ 39 cited

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

Kamaljyoti Nath, Xuhui Meng, Daniel J Smith +1

This paper presents a physics-informed neural network (PINN) approach for monitoring the health of diesel engines. The aim is to evaluate the engine dynamics, identify unknown para…

cs.LG2021

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

Amartya Dutta, Kamaljyoti Nath

Bridge health monitoring using machine learning tools has become an efficient and cost-effective approach in recent times. In the present study, strains in railway bridge member, a…