10 citations · 15 across the 3 of their papers we have counts for
3 papers
physics.comp-ph2020★ 2 cited
Uncertainty Quantification of Locally Nonlinear Dynamical Systems using Neural Networks
Subhayan De
Models are often given in terms of differential equations to represent physical systems. In the presence of uncertainty, accurate prediction of the behavior of these systems using…
stat.ML2020★ 10 cited
On transfer learning of neural networks using bi-fidelity data for uncertainty propagation
Subhayan De, Jolene Britton, Matthew Reynolds +3
Due to their high degree of expressiveness, neural networks have recently been used as surrogate models for mapping inputs of an engineering system to outputs of interest. Once tra…
math.OC2019★ 3 cited
Bi-fidelity Stochastic Gradient Descent for Structural Optimization under Uncertainty
Subhayan De, Kurt Maute, Alireza Doostan
The presence of uncertainty in material properties and geometry of a structure is ubiquitous. The design of robust engineering structures, therefore, needs to incorporate uncertain…