activity
20172021
most citedRobust Training and Initialization of Deep Neural Networks: An Adaptive Basis Viewpoint

17 citations · 35 across the 9 of their papers we have counts for

collaborators

12 papers

math.AP2021

Fractional Modeling in Action: A Survey of Nonlocal Models for Subsurface Transport, Turbulent Flows, and Anomalous Materials

Jorge Suzuki, Mamikon Gulian, Mohsen Zayernouri +1

Modeling of phenomena such as anomalous transport via fractional-order differential equations has been established as an effective alternative to partial differential equations, du…

cs.LG20213 cited

Probabilistic partition of unity networks: clustering based deep approximation

Nat Trask, Mamikon Gulian, Andy Huang +1

Partition of unity networks (POU-Nets) have been shown capable of realizing algebraic convergence rates for regression and solution of PDEs, but require empirical tuning of trainin…

cond-mat.soft20217 cited

Distribution and pressure of active Lévy swimmers under confinement

Tingtao Zhou, Zhiwei Peng, Mamikon Gulian +1

Many active matter systems are known to perform Lévy walks during migration or foraging. Such superdiffusive transport indicates long-range correlated dynamics. These behavior patt…

cs.LG20216 cited

Partition of unity networks: deep hp-approximation

Kookjin Lee, Nathaniel A. Trask, Ravi G. Patel +2

Approximation theorists have established best-in-class optimal approximation rates of deep neural networks by utilizing their ability to simultaneously emulate partitions of unity…

math.AP2021

Analysis of Anisotropic Nonlocal Diffusion Models: Well-posedness of Fractional Problems for Anomalous Transport

Marta D'Elia, Mamikon Gulian

We analyze the well-posedness of an anisotropic, nonlocal diffusion equation. Establishing an equivalence between weighted and unweighted anisotropic nonlocal diffusion operators i…

cs.LG2020

Gaussian Process Regression constrained by Boundary Value Problems

Mamikon Gulian, Ari Frankel, Laura Swiler

We develop a framework for Gaussian processes regression constrained by boundary value problems. The framework may be applied to infer the solution of a well-posed boundary value p…