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20182021
most citedA comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations

34 citations · 37 across the 5 of their papers we have counts for

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2 papers · 1 filter

math.NA2021

Time-dependent stochastic basis adaptation for uncertainty quantification

Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky

We extend stochastic basis adaptation and spatial domain decomposition methods to solve time varying stochastic partial differential equations (SPDEs) with a large number of input…

math.NA2020

An efficient epistemic uncertainty quantification algorithm for a class of stochastic models: A post-processing and domain decomposition framework

Mahadevan Ganesh, Stuart C Hawkins, Alexandre Tartakovsky +1

Partial differential equations (PDEs) are fundamental for theoretically describing numerous physical processes that are based on some input fields in spatial configurations. Unders…