Mamikon Gulian | DeepAI

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Featured Co-authors

Yue Yu
98 publications
Paris Perdikaris
43 publications
Marta D'Elia
26 publications
Maziar Raissi
21 publications
Nathaniel Trask
20 publications
Kookjin Lee
18 publications
Huaiqian You
14 publications
Joseph Hart
12 publications
Mohsen Zayernouri
10 publications
George Karniadakis
9 publications
Ravi G. Patel
8 publications

∙ 03/20/2023

Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via Operator Learning with Limited Data

In complex large-scale systems such as climate, important effects are ca...

0 Joseph Hart, et al. ∙

∙ 04/22/2022

Error-in-variables modelling for operator learning

Deep operator learning has emerged as a promising tool for reduced-order...

0 Ravi G. Patel, et al. ∙

∙ 10/22/2021

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

Modeling of phenomena such as anomalous transport via fractional-order d...

0 Jorge Suzuki, et al. ∙

∙ 07/07/2021

Probabilistic partition of unity networks: clustering based deep approximation

Partition of unity networks (POU-Nets) have been shown capable of realiz...

45 Nat Trask, et al. ∙

∙ 01/12/2021

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

We analyze the well-posedness of an anisotropic, nonlocal diffusion equa...

0 Marta D'Elia, et al. ∙

∙ 12/22/2020

Gaussian Process Regression constrained by Boundary Value Problems

We develop a framework for Gaussian processes regression constrained by ...

0 Mamikon Gulian, et al. ∙

∙ 06/16/2020

A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges

Gaussian process regression is a popular Bayesian framework for surrogat...

0 Laura Swiler, et al. ∙

∙ 05/17/2020

Data-driven learning of robust nonlocal physics from high-fidelity synthetic data

A key challenge to nonlocal models is the analytical complexity of deriv...

0 Huaiqian You, et al. ∙

∙ 08/02/2018

Machine Learning of Space-Fractional Differential Equations

Data-driven discovery of "hidden physics" -- i.e., machine learning of d...

0 Mamikon Gulian, et al. ∙

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