1 citations · 3 across the 6 of their papers we have counts for
6 papers
Trust-Aware Multimodal Data Fusion for Yield Estimation: A Case Study of the 2020 Beirut Explosion
Lekha Patel, Craig Ulmer, Stephen J. Verzi +4
The estimation of explosive yield from heterogeneous observational data presents fundamental challenges in inverse problems, particularly when combining traditional physical measur…
Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantification
J. Hart, I. Manickam, M. Gulian +6
Stratospheric aerosols play an important role in the earth system and can affect the climate on timescales of months to years. However, estimating the characteristics of partially…
Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via Operator Learning with Limited Data
Joseph Hart, Mamikon Gulian, Indu Manickam +1
In complex large-scale systems such as climate, important effects are caused by a combination of confounding processes that are not fully observable. The identification of sources…
Error-in-variables modelling for operator learning
Ravi G. Patel, Indu Manickam, Myoungkyu Lee +1
Deep operator learning has emerged as a promising tool for reduced-order modelling and PDE model discovery. Leveraging the expressive power of deep neural networks, especially in h…
Thermodynamically consistent physics-informed neural networks for hyperbolic systems
Ravi G. Patel, Indu Manickam, Nathaniel A. Trask +4
Physics-informed neural network architectures have emerged as a powerful tool for developing flexible PDE solvers which easily assimilate data, but face challenges related to the P…
IdeoTrace: A Framework for Ideology Tracing with a Case Study on the 2016 U.S. Presidential Election
Indu Manickam, Andrew S. Lan, Gautam Dasarathy +1
The 2016 United States presidential election has been characterized as a period of extreme divisiveness that was exacerbated on social media by the influence of fake news, trolls,…