Resources

covered subject areas

STRUREL (4 entries)

Expand your knowledge through our resources. Try our online tools and start reading our articles about probabilistic modeling, machine learning, risk assessment, uncertainty quantification and other fundamental topics. Here are the resources you need to get started dealing with uncertainties that arise in your day-to-day projects.

Machine learning

Research paper: Speed-power models – a Bayesian approach

A data-driven approach is used to learn speed-power curves probabilistically. These models can then be used to assess the performance of a vessel.

Reliability analysis

What is the 'safety margin' in structural reliability analysis?

The difference between the capacity and the demand is the safety margin.

Probabilistic modeling

Does it make a difference whether or not a discretized Poisson process is used in Bayesian post-processing?

Even though the underlying posterior distributions are of different type, the results will be the same if the rate of the Poisson process is sufficiently small and weakly informative and consistent priors are used.

Probabilistic modeling

What prior to use when learning the rate of a Poisson process?

A weakly informative prior that follows a Gamma distribution with shape parameter 1 and rate parameter 2 is a suitable choice for many problems. Working with informative prior distributions requires careful handling and a close look at the quantiles.

Machine learning

Research paper: Data-driven predictive maintenance for gas distribution networks

A data-driven approach that uses all available information to forecast the defect rate of pipes in a gas distribution network is presented.

Probabilistic modeling

How to quantify the uncertainty about the rate of a Poisson process?

We explain a Bayesian post-processing step for learning the rate of a Poisson process. It is ideally suited to quantify the uncertainty and to evaluate credible intervals.

Probabilistic modeling

What is a Poisson process?

A Poisson process is used to model the occurrence of independent random events along a continuous axis. It is a very important stochastic process in probabilistic modeling.

Reliability analysis

What is the 'basic structural reliability problem'?

When the limit state function can be expressed as the difference between capacity and demand, the problem is sometimes referred to as basic reliability problem.

ONLINE TOOL

Reliability analysis

Post-processing of Monte Carlo simulation

Use our web app to quantify the uncertainty about the probability of failure based on a conducted Monte Carlo simulation.

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Reliability analysis

Why should you avoid Normal confidence intervals for Monte Carlo simulation?

The underlying distribution can be highly skewed, even if the total number of samples in the Monte Carlo simultion is very large. This is why the Normal approximation often performs poorly in practice.

covered subject areas

Reliability analysis (16 entries)

Sensitivity analysis (11 entries)

Probabilistic modeling (10 entries)

STRUREL (4 entries)

Machine learning (2 entries)

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