|
Identifying-a-system-based-manoeuvring-model-with-virtual-captive-tests
|
|
VesselManoeuvringModels
|
|
phd-thesis
|
|
PHD
|
|
Sensor-Fusion-and-Non-linear-Filtering-for-Automotive-Systems
|
|
inviscid_CFD_-roll_decay
|
|
System-identification-of-Vessel-Manoeuvring-Models
|
|
System-identification-of-Vessel-Manoeuvring-Models-LaTex
|
|
jupyter-book
|
|
System-identification-for-a-physically-correct-ship-manoeuvring-model-in-wind-conditions
|
|
Kalman-and-Bayesian-Filters-in-Python
|
|
SJO747-Wave-Loads-and-Seakeeping
|
|
PHD_literature_review
|
|
System-identification-for-a-physically-correct-ship-manoeuvring-model-in-wind-conditions_martin
|
|
kedro
|
|
MMS185-Stability-and-design-basis-for-marine-structures
|
|
pydy-tutorial-human-standing
|
|
Adopting-a-laboratory-system-based-manoeuvring-model-to-a-real-sea-environment
|
|
UMV-HLAR
|
|
BookCode_Edition1
|
|
lillatrampoli.se
|
|
blocket
|
|
rolldecay-estimators
|
|
water_depth
|
|
MyST-NB
|
|
DNV-NORDIC-MARITIME-UNIVERSITIES-WORKSHOP-2023
|
|
A-semi-empirical-method-for-predicting-roll-damping-based-on-machine-learning
|
|
licentiate-thesis
|
|
curve_fit
|
|
PythonVehicleSimulator
|
|
next
|