We are pleased to invite you to the workshop “Recent Advancements in Uncertainty Quantification and Propagation in Civil Engineering and Geodetic Science”, taking place on Tuesday, June 9, 2026.
The workshop is organized together with the Geodetic Institute and will be held in conjunction with the visit of our esteemed guest, mathematician and uncertainty expert Prof. Dr. Vladik Kreinovich from the University of Texas at El Paso.
The meeting time is set for 09:15, with the workshop starting at 09:30.
Keynote Talk
Prof. Dr. Vladik Kreinovich
University of Texas at El Paso
Title:
How to represent and process uncertainty in practical situations: towards a feasible combination of probabilistic, interval, and fuzzy uncertainty.
In the very beginning of the data processing process, as raw data, we have measurement results and expert estimates. Measurements are never 100% accurate; there is usually a difference between the measurement result and the unknown actual value of the measured quantity. This difference is known as the measurement error. For a measurement result, sometimes we know the probability distribution of the measurement error, but often all we know is the upper bound on the absolute value of the measurement error — in which case we have interval uncertainty.
For expert estimates, we can have different upper bounds with different degrees of confidence, which forms what is known as a fuzzy number. In a few cases, we directly deal with raw data, but usually, when we process data, we deal with the results of previous data processing that typically uses results with all three types of uncertainty. Thus, to describe both inputs and outputs of data processing, all three types of uncertainty need to be taken into account.
In many cases, measurement errors are small, so terms that are quadratic and of higher order with respect to these errors can be safely ignored, and only linear terms need to be considered. In this case, the resulting uncertainty can be described as the sum of the three types of uncertainty, which can therefore be processed separately.
This is how we envision a number of the future: a tuple consisting of the numerical result of data processing plus three uncertainty measures: an upper bound for the interval part of the uncertainty, a standard deviation for the probabilistic part, and an interval corresponding, for example, to the triangular form of the fuzzy uncertainty part.
But this is not all: the same raw data values are often used to compute different inputs to our data processing algorithm. In this case, even when all original measurement errors are independent, there is a correlation between the inputs. Therefore, to properly describe uncertainty, it is not enough to describe the numbers themselves; it is also important to describe relations between different numbers.
In the probabilistic case, this can be described by correlation. In the interval case, it can be proven that the simplest way to describe such a relation is by an ellipsoid, so for every two numbers, we describe the parameters of the corresponding ellipse. For the fuzzy part, it is also reasonable to use the corresponding ellipse. In the linearized case, this enables us, for example, to avoid overestimation — the main problem of traditional interval computation techniques.
Now we have a more adequate picture of numbers of the future: numbers plus relations between pairs of numbers. This is similar to the transition in physics in the 1960s. Before that, we had a hierarchical model of matter: molecules consist of atoms, atoms consist of elementary particles, and we can separate atoms and particles and study the properties of each component. In modern physics, for example, a proton consists of quarks, but quarks cannot be separated; we always need to take their interaction into account. Similarly, we cannot reduce the description of uncertainty by simply describing the uncertainty of each number; we need to take relations into account.
Another analogy is quantum computing, where it is crucial to use and take into account entanglement between states of individual objects. In the future, when quantum computing becomes ubiquitous, we will need to add a fourth uncertainty component to the description of numbers and their relations — a component corresponding to quantum uncertainty.
And, of course, in situations when measurement errors are larger and quadratic terms can no longer be ignored, we need to take into account relations between different types of uncertainty, as described, for example, by p-boxes and related techniques.
Schedule
09:15 – 09:30
Meet & Have Coffee/Tea + Snacks
Location: Kaserne 1. OG Foyer and IRZ Library
09:30 – 09:35
Opening Words
Prof. Neumann / Prof. Beer
09:35 – 10:05
Presentation 1
Yue Hu, Ph.D.
Learning 3D Auto-Correlation Function Directly from Sparse Measurement Data: A Spectral Approach
10:05 – 10:35
Presentation 2
Jiahui Fu, M.Sc.
Probability-Compatible Modeling and Efficient Representation of 3D Multivariate Non-Gaussian Random Fields via Stochastic Harmonic Functions
10:35 – 10:50
Discuss and Have Some Coffee/Tea + Snacks
Location: Kaserne 1. OG Foyer and IRZ Library
10:50 – 11:20
Presentation 3
Jan Hartmann, M.Sc.
Modeling the Distribution of Terrestrial Laser Scanner Distance-Related Uncertainties for Calibration of Geodetic Sensors
11:20 – 11:50
Presentation 4
Mengze Lyu, Ph.D.
Reconstructing Hidden Dynamics: A Full-Probabilistic Framework for Unobserved States under Incomplete Measurements
11:50 – 12:50
Lunch Break
Location: Hauptmensa
12:50 – 13:05
Meet, Discuss and Have Some Coffee/Tea
Location: Foyer 1. OG Kaserne, IRZ Library
13:05 – 13:50
Presentation 5 – Keynote Talk
Prof. Vladik Kreinovich
How to represent and process uncertainty in practical situations: towards a feasible combination of probabilistic, interval, and fuzzy uncertainty.
13:50 – 15:20
Presentation 6
Dr.-Ing. Yi Luo
Unified Framework for Hybrid Aleatory and Epistemic Uncertainty Propagation via Decoupled Multi-Probability Density Evolution Method
15:20 – 15:30
Discuss and Have Some Coffee/Tea + Snacks
Location: Kaserne 1. OG Foyer and IRZ Library
15:30 – 16:00
Presentation 7
Lukas Fritsch, M.Sc.
Variational Bayesian Inference with Transport Maps
16:00 – 16:30
Presentation 8
Niklas R. Winnewisser, M.Sc.
Pattern Recognition in Damage Evolutions Using Knowledge Graph-Modeling and Variational Graph Autoencoders: A Comparison to Well-Known Approaches
16:30 – 17:00
Have Some Coffee/Tea + Snacks
Location: Kaserne 1. OG Foyer and IRZ Library
17:00 – 17:30
Presentation 9
Wei Shen, Ph.D.
Sequential Reliability-Based Optimization of Frame Structure under Global Buckling Constraint
17:30 – 18:00
Discussion and Closing of the Workshop
Niklas R. Winnewisser / Prof. Beer
Additional Information
The workshop will take place in the Institute for Risk and Reliability’s library, room 116, in building 3407 “Alte Kaserne”, Callinstraße 34, 30167 Hannover.
The workshop will also be available via Webex online meeting. Participants who would like to attend online can join via:
For questions regarding online participation, please contact Niklas R. Winnewisser.
