Mario Rüttgers

Walter-Benjamin Fellow (DFG) and Visiting professor

Inha University

South Korea

Research Interests

Explore related searches

Professors in South Korea Professors in Fluid Mechanics Fluid Mechanics professors in South Korea

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Mario Rüttgers is a Walter-Benjamin Fellow (DFG) and Visiting Professor at Inha University, South Korea. His research focuses on computational fluid dynamics, machine learning applications in healthcare, and predictive modeling of environmental phenomena. Recent articles highlight his work on automated virtual surgery planning, lattice-Boltzmann simulations for cerebral aneurysms, and the use of neural networks in wind power efficiency and typhoon prediction.

Articles (8)

Year:

2026

Year:

2025

Year:

2024

Year:

2024

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Collaborators (4)

Makoto Tsubokura

Professor

Kobe University

JAPAN

View details

Martin Brede

University of Rostock

GERMANY

View details

Wolfgang Schröder

RWTH Aachen University

GERMANY

View details

Sangseung Lee

Inha University

SOUTH KOREA

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Mario Rüttgers

Research Interests

Explore related searches

Contact this professor

About

Articles (8)

Towards a widespread usage of computational fluid dynamics simulations for automated virtual nasal surgery planning

Patient-specific lattice-Boltzmann simulations with inflow conditions from magnetic resonance velocimetry measurements for analyzing cerebral aneurysms

Automated surgery planning for an obstructed nose by combining computational fluid dynamics with reinforcement learning

Refining computer tomography data with super-resolution networks to increase the accuracy of respiratory flow simulations

Effects of spatiotemporal correlations in wind data on neural network-based wind predictions

Neural Networks for Improving Wind Power Efficiency: A Review

A machine-learning-based method for automatizing lattice-Boltzmann simulations of respiratory flows

Prediction of Typhoon Track and Intensity Using a Generative Adversarial Network With Observational and Meteorological Data

Collaborators (4)

Makoto Tsubokura

Martin Brede

Wolfgang Schröder

Sangseung Lee

Make your next move.