Philip Avery

Stanford University

H-index: 26

North America-United States

About Philip Avery

Philip Avery, With an exceptional h-index of 26 and a recent h-index of 20 (since 2020), a distinguished researcher at Stanford University, specializes in the field of Computational Mechanics.

His recent articles reflect a diverse array of research interests and contributions to the field:

Bottom-up hierarchical and categorical metacomputing for automating composition and deployment of directly computable multiphysics models

Acceleration of a physics-based machine learning approach for modeling and quantifying model-form uncertainties and performing model updating

A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling

Update: Modeling Supersonic Parachute Inflations for Mars Spacecraft

Validation of a high-fidelity supersonic parachute inflation dynamics model and best practice

Dimensionality Reduction of Embedded Boundary Models for Problems with Large Shape Changes

A computationally tractable framework for nonlinear dynamic multiscale modeling of membrane woven fabrics

An embedded boundary approach for resolving the contribution of cable subsystems to fully coupled fluid‐structure interaction

Philip Avery Information

University	Stanford University
Position	Research Engineer
Citations(all)	2239
Citations(since 2020)	1295
Cited By	1431
hIndex(all)	26
hIndex(since 2020)	20
i10Index(all)	35
i10Index(since 2020)	27
Email	Access Email
University Profile Page	Stanford University
Google Scholar	View Google Scholar Profile

Philip Avery Skills & Research Interests

Computational Mechanics

Top articles of Philip Avery

Title	Journal	Author(s)	Publication Date
Bottom-up hierarchical and categorical metacomputing for automating composition and deployment of directly computable multiphysics models	Journal of Computational Science	JG Michopoulos AP Iliopoulos C Farhat P Avery G Daeninck ...	2024/4/18
Acceleration of a physics-based machine learning approach for modeling and quantifying model-form uncertainties and performing model updating	Journal of Computing and Information Science in Engineering	Marie-Jo Azzi Chady Ghnatios Philip Avery Charbel Farhat	2023/2/1
A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling	International Journal for Numerical Methods in Engineering	Faisal As' ad Philip Avery Charbel Farhat	2022/6/30
Update: Modeling Supersonic Parachute Inflations for Mars Spacecraft		Jason Rabinovitch Faisal As’ ad Philip Avery Charbel Farhat Navid Ataei ...	2022
Validation of a high-fidelity supersonic parachute inflation dynamics model and best practice		Jason Rabinovitch Marcus Lobbia Charbel Farhat Faisal As' ad Philip Avery	2022/1/3
Dimensionality Reduction of Embedded Boundary Models for Problems with Large Shape Changes		Noah Youkilis Philip Avery Charbel Farhat	2022
A computationally tractable framework for nonlinear dynamic multiscale modeling of membrane woven fabrics	International Journal for Numerical Methods in Engineering	Philip Avery Daniel Z Huang Wanli He Johanna Ehlers Armen Derkevorkian ...	2021/5/30
An embedded boundary approach for resolving the contribution of cable subsystems to fully coupled fluid‐structure interaction	International Journal for Numerical Methods in Engineering	Daniel Z Huang Philip Avery Charbel Farhat	2021/10/15
In situ adaptive reduction of nonlinear multiscale structural dynamics models	International Journal for Numerical Methods in Engineering	Wanli He Philip Avery Charbel Farhat	2020/11/30
Parametric studies of aerodynamic properties of wings using various forms of machine learning		Charbel Farhat Nahla Alhazmi Phil Avery Radek Tezaur Yousef Ghazi	2020/3/19
Modeling, simulation and validation of supersonic parachute inflation dynamics during Mars landing		Daniel Z Huang Philip Avery Charbel Farhat Jason Rabinovitch Armen Derkevorkian ...	2020