Description

First annual meeting of the Dutch Inverse Problems Community

The first annual meeting of the Dutch Inverse Problems Community will take place on 25-26 November 2021 in conference center de Werelt, Lunteren.

 Registration

The registration fee for this 2-day event is EUR 235, which includes hotel and catering. Registering for a single day only is possible as well and costs EUR 50, which includes lunch.

You can register here. Currently, the registration is limited to 25 people and is on a first-come-first-served basis. If registration is full, we will put you on a waiting list and contact you if extra spots become available.

 Preliminary program:

Thursday 25 November

09.30 - 10.00: Reception, coffee

10.00 - 12.30: Masterclasses 1 & 2 (in parallel)

12.30 - 14.00: Lunch break and poster session

14.00 - 16.30: Masterclasses 1 & 2 (in parallel)

16.30 - 17.30: Drinks and poster session

17:30 - 18:30: Brainstorm session on joint grant proposals etc.

18.30 -           : Dinner, followed by social program

Friday 26 November

09.00 - 09.30: Reception, coffee

09.30 - 10.30: Plenary talks

10.30 - 11.00: Coffee break

11.00 - 12.00: Plenary talks

12.00 - 13.30: Lunch break

13.30 - 14.30: Plenary talks

14:30 - 16:30: Panel discussion

 Masterclasses

The masterclasses are meant to introduce the participants to a particular topic by a combination of lecture and hands-on exercises. Each masterclass lasts to whole day (10:00 - 16:30 with lunch break), so you can register for one masterclass. During registration you will be asked to pick a top 3 from the topics below. As we can only offer two masterclasses, you may not be assigned your first pick.

Basic inverse problems and imaging theory - Martin van Gijzen This master class will discuss the theory of linear discrete inverse problems with application to tomographic image reconstruction.  Linear discrete inverse problems can be formulated as a least-squares problem. This least-squares problem is typically ill-posed, which means that it does not have a unique solution, or that the solution is very sensitive to noise. Consequently, we can not expect that a standard technique like solving an ill-posed least-squares problem by solving the normal equations will lead to a relevant solution. However, the solution of such problems are of pivotal importance in many applications. The class starts with explaining how tomographic image reconstruction yields an ill-posed least-squares problem. Then we review linear algebra techniques for solving such problems. We will introduce the concept of regularisation to make the problem well-posed, and discuss a number of ideas of how to regularise. Finally, we will present and study solution algorithms. The class finishes with an assignment in which the theory and algorithms are applied to a small but very illustrative tomographic reconstruction problem that models wave propagation in the earth crust. Entry requirements: Basic knowledge of linear algebra.

Optimisation techniques in inverse problems - Juan Peypouquet In this course, we will discuss how some optimisation techniques can be used to analyse and solve a class of inverse problems. The course will contain a self-contained review of convex analysis, subdifferential calculus and optimality conditions, plus an introduction to iterative methods used to solve these kinds of problems. Entry requirements: Bachelor-level knowledge of functional analysis and differential calculus is recommended

Data assimilation - Femke Vossepoel Data assimilation combines dynamic models with available observations to find the probability distribution of the model solution given the data. In the last decades, we see a growing application of data assimilation in the geosciences, but also in other fields, from economics to epidemiology. In this master class, we will explain the principles of data assimilation from a Bayesian perspective and provide a unified formulation of data assimilation that places various data assimilation methods and their applications in perspective. We will discuss how to use data assimilation for state and parameter estimation and we will discuss how these methods can deal with errors in the dynamic model and its control or forcing. Participants will experience the possibilities and limitations of data assimilation in an exercise with a toy problem. Entry requirements: MSc-level education in physics, maths, engineering or geosciences. Basic knowledge of inverse problem theory and Bayesian statistics.

Fundamentals of Acoustic and Electromagnetic Wave Field Theory - Koen van Dongen & Rob Remis 

• Email: Tristan van Leeuwen
• iCal

PhD defense Rien Lagerwerf

• Email: Rien Lagerwerf
• iCal