BEM BEMT Blade Element Momentum Theory Propeller Optimization Propeller Performance

Feed-forward neural network Machine learning MATLAB Medical application Monte Carlo simulations Morphoelasticity Post-burn scar contraction python

chloride Delta estuary Forecasting hydrology LSTM Machine learning river salt intrusion Water Management

PIV (Particle Image Velocimetry) Uncertainty Quantification

coastal waters Infragravity waves inland waters oceanic waters oceanography Simulating WAves Nearshore (SWAN)

GNN Graph neural networks Machine learning Metamodel Surrogate model SWMM Time Series Urban drainage systems

Big data High performance computing Optimized data handling

Graphs Graph Theory Hierarchical Graphs Hierarchical Networks Julia Language Julia Package Multilayer Graphs Multilayer Networks Multilevel Graphs Multilevel Networks Networks Network Science Network Theory

Text analysis & natural language processing

All software

WRFpy

Blade Element Momentum Theory Code for Positive and Negative Thrust Propellers

Code supporting the paper: A Bayesian finite-element trained machine learning approach for predicting post-burn contraction

Code underlying the publication: Forecasting estuarine salt intrusion in the Rhine-Meuse delta using an LSTM model

MATLAB scripts created during the work on "Multi-Δt approach for peak-locking error correction and uncertainty quantification in PIV"

Source code of the SWAN model: Free Infragravity Waves in the North Sea

SWMM_GNN_Repository_Paper_version

Bacting

ece2cmor3

jaspProcess

MultilayerGraphs.jl

nlppln