José del Águila Ferrandis

About José del Águila Ferrandis

José del Águila Ferrandis, With an exceptional h-index of 6 and a recent h-index of 6 (since 2020), a distinguished researcher at Massachusetts Institute of Technology, specializes in the field of Machine Learning, Non-linear motions, Fluid Structure Interaction..

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

Improving predictions of vortex induced vibrations via generalizable hydrodynamic databases across several current incidence angles

Architected materials for artificial reefs to increase storm energy dissipation

Physics-based Data-informed Prediction of Vertical, Catenary, and Stepped Riser Vortex-induced Vibrations

Modular morphing lattices for large-scale underwater continuum robotic structures

Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors

Optimized parametric hydrodynamic databases provide accurate response predictions and describe the physics of vortex-induced vibrations

Inferring Optimal Hydrodynamic Databases for Vortex Induced Cross Flow Vibration Prediction of Marine Risers Using Limited Sensor Measurements

Learning functional priors and posteriors from data and physics

José del Águila Ferrandis Information

University

Position

___

Citations(all)

340

Citations(since 2020)

340

Cited By

28

hIndex(all)

6

hIndex(since 2020)

6

i10Index(all)

4

i10Index(since 2020)

4

Email

University Profile Page

Google Scholar

José del Águila Ferrandis Skills & Research Interests

Machine Learning

Non-linear motions

Fluid Structure Interaction.

Top articles of José del Águila Ferrandis

Improving predictions of vortex induced vibrations via generalizable hydrodynamic databases across several current incidence angles

Journal of Fluids and Structures

2024/5/1

José Del Águila Ferrandis
José Del Águila Ferrandis

H-Index: 4

Dixia Fan
Dixia Fan

H-Index: 7

Themistoklis Sapsis
Themistoklis Sapsis

H-Index: 30

Architected materials for artificial reefs to increase storm energy dissipation

PNAS nexus

2024/3

Physics-based Data-informed Prediction of Vertical, Catenary, and Stepped Riser Vortex-induced Vibrations

International Journal of Offshore and Polar Engineering

2023/12/15

José Del Águila Ferrandis
José Del Águila Ferrandis

H-Index: 4

Themistoklis Sapsis
Themistoklis Sapsis

H-Index: 30

Dixia Fan
Dixia Fan

H-Index: 7

Modular morphing lattices for large-scale underwater continuum robotic structures

Soft Robotics

2023/8/1

Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors

Ocean Engineering

2022/12/15

José Del Águila Ferrandis
José Del Águila Ferrandis

H-Index: 4

Themistoklis Sapsis
Themistoklis Sapsis

H-Index: 30

Dixia Fan
Dixia Fan

H-Index: 7

Optimized parametric hydrodynamic databases provide accurate response predictions and describe the physics of vortex-induced vibrations

Journal of Fluids and Structures

2022/7/1

Inferring Optimal Hydrodynamic Databases for Vortex Induced Cross Flow Vibration Prediction of Marine Risers Using Limited Sensor Measurements

2022/6/5

José Del Águila Ferrandis
José Del Águila Ferrandis

H-Index: 4

Dixia Fan
Dixia Fan

H-Index: 7

Themistoklis Sapsis
Themistoklis Sapsis

H-Index: 30

Learning functional priors and posteriors from data and physics

Journal of Computational Physics

2022/5/15

Learning optimal parametric hydrodynamic database for vortex-induced crossflow vibration prediction of both freely-mounted rigid and flexible cylinders

2021/6/20

NVIDIA SimNet™: An AI-accelerated multi-physics simulation framework

2021/6/9

Influence of viscosity and non-linearities in predicting motions of a wind energy offshore platform in regular waves

Journal of Offshore Mechanics and Arctic Engineering

2020/12/1

José Del Águila Ferrandis
José Del Águila Ferrandis

H-Index: 4

Luca Bonfiglio
Luca Bonfiglio

H-Index: 11

Learning functionals via LSTM neural networks for predicting vessel dynamics in extreme sea states

Proceedings of the Royal Society A

2021/1/27

José Del Águila Ferrandis
José Del Águila Ferrandis

H-Index: 4

SimNet: A neural network solver for multi-Physics applications

APS Division of Fluid Dynamics Meeting Abstracts

2020

See List of Professors in José del Águila Ferrandis University(Massachusetts Institute of Technology)

Co-Authors

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