51 citations · 155 across the 19 of their papers we have counts for
10 papers · 1 filter
Importance of localized dilatation and distensibility in identifying determinants of thoracic aortic aneurysm with neural operators
David S. Li, Somdatta Goswami, Qianying Cao +4
Thoracic aortic aneurysms (TAAs) arise from diverse mechanical and mechanobiological disruptions to the aortic wall that increase the risk of dissection or rupture. Evidence links…
Neural Operators for Stochastic Modeling of Nonlinear Structural System Response to Natural Hazards
Somdatta Goswami, Dimitris G. Giovanis, Bowei Li +2
Traditionally, neural networks have been employed to learn the mapping between finite-dimensional Euclidean spaces. However, recent research has opened up new horizons, focusing on…
Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem Solving
Varun Kumar, Somdatta Goswami, Katiana Kontolati +2
Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-ta…
Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning
Luis Mandl, Somdatta Goswami, Lena Lambers +1
The deep operator network (DeepONet) is a popular neural operator architecture that has shown promise in solving partial differential equations (PDEs) by using deep neural networks…
A Resolution Independent Neural Operator
Bahador Bahmani, Somdatta Goswami, Ioannis G. Kevrekidis +1
The Deep Operator Network (DeepONet) is a powerful neural operator architecture that uses two neural networks to map between infinite-dimensional function spaces. This architecture…
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…