2.1k citations · 2.9k across the 29 of their papers we have counts for
9 papers · 1 filter
DON-LSTM: Multi-Resolution Learning with DeepONets and Long Short-Term Memory Neural Networks
Katarzyna Michałowska, Somdatta Goswami, George Em Karniadakis +1
Deep operator networks (DeepONets, DONs) offer a distinct advantage over traditional neural networks in their ability to be trained on multi-resolution data. This property becomes…
Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators
Nikolas Borrel-Jensen, Somdatta Goswami, Allan P. Engsig-Karup +2
We address the challenge of sound propagation simulations in 3D virtual rooms with moving sources, which have applications in virtual/augmented reality, game audio, and spatial com…
Learning in latent spaces improves the predictive accuracy of deep neural operators
Katiana Kontolati, Somdatta Goswami, George Em Karniadakis +1
Operator regression provides a powerful means of constructing discretization-invariant emulators for partial-differential equations (PDEs) describing physical systems. Neural opera…
Real-Time Prediction of Gas Flow Dynamics in Diesel Engines using a Deep Neural Operator Framework
Varun Kumar, Somdatta Goswami, Daniel J. Smith +1
We develop a data-driven deep neural operator framework to approximate multiple output states for a diesel engine and generate real-time predictions with reasonable accuracy. As em…
Developing a cost-effective emulator for groundwater flow modeling using deep neural operators
Maria Luisa Taccari, He Wang, Somdatta Goswami +3
Current groundwater models face a significant challenge in their implementation due to heavy computational burdens. To overcome this, our work proposes a cost-effective emulator th…
LNO: Laplace Neural Operator for Solving Differential Equations
Qianying Cao, Somdatta Goswami, George Em Karniadakis
We introduce the Laplace neural operator (LNO), which leverages the Laplace transform to decompose the input space. Unlike the Fourier Neural Operator (FNO), LNO can handle non-per…
Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks
Katarzyna Michałowska, Somdatta Goswami, George Em Karniadakis +1
Deep neural networks are an attractive alternative for simulating complex dynamical systems, as in comparison to traditional scientific computing methods, they offer reduced comput…
Learning stiff chemical kinetics using extended deep neural operators
Somdatta Goswami, Ameya D. Jagtap, Hessam Babaee +2
We utilize neural operators to learn the solution propagator for the challenging chemical kinetics equation. Specifically, we apply the deep operator network (DeepONet) along with…
Deep neural operators can predict the real-time response of floating offshore structures under irregular waves
Qianying Cao, Somdatta Goswami, Tapas Tripura +2
The use of neural operators in a digital twin model of an offshore floating structure can provide a paradigm shift in structural response prediction and health monitoring, providin…