39 citations · 95 across the 8 of their papers we have counts for
9 papers
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…
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…
A Neural Operator Emulator for Coastal and Riverine Shallow Water Dynamics
Peter Rivera-Casillas, Sourav Dutta, Shukai Cai +7
Coastal regions and river floodplains are particularly vulnerable to the impacts of extreme weather events. Accurate real-time forecasting of hydrodynamic processes in these areas…
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…
Learning thermoacoustic interactions in combustors using a physics-informed neural network
Sathesh Mariappan, Kamaljyoti Nath, George Em Karniadakis
We introduce a physics-informed neural network (PINN) method to study thermoacoustic interactions leading to combustion instability in combustors. Specifically, we employ a PINN to…
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…
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…
A Causality-DeepONet for Causal Responses of Linear Dynamical Systems
Lizuo Liu, Kamaljyoti Nath, Wei Cai
In this paper, we propose a DeepONet structure with causality to represent the causal linear operators between Banach spaces of time-dependent signals. The theorem of universal app…
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…