Prof. Francesca Gardini, Università di Pavia
Tuesday, April 30, 2024, 16:00
- 17:00
Building 1, Level 3, Room 3119
We will discuss the solution of eigenvalue problems associated with partial differential equations (PDE)s that can be written in the generalised form Ax = λMx, where the matrices A and/or M may depend on a scalar parameter. Parameter dependent matrices occur frequently when stabilised formulations are used for the numerical approximation of PDEs. With the help of classical numerical examples we will show that the presence of one (or both) parameters can produce unexpected results.
Prof. Silvia Bertoluzza
Tuesday, March 05, 2024, 16:00
- 17:00
Building 2, Level 5, Room 5209
We present a theoretical analysis of the Weak Adversarial Networks (WAN) method, recently proposed in [1, 2], as a method for approximating the solution of partial differential equations in high dimensions and tested in the framework of inverse problems. In a very general abstract framework.
Prof. Christof Schmidhuber, ZHAW School of Engineering
Tuesday, February 27, 2024, 16:00
- 17:00
Building 9, Level 2, Room 2322
Analogies between financial markets and critical phenomena have long been observed empirically. So far, no convincing theory has emerged that can explain these empirical observations. Here, we take a step towards such a theory by modeling financial markets as a lattice gas.
Prof. Dr. Victorita Dolean, Mathematics and Computer Science, Scientific Computing, TU Eindhoven
Tuesday, February 06, 2024, 16:00
- 17:00
Building 2, Level 5, Room 5220
Wave propagation and scattering problems are of huge importance in many applications in science and engineering - e.g., in seismic and medical imaging and more generally in acoustics and electromagnetics.
Prof. Zhiming Chen, Academy of mathematics and Systems Science, Chinese Academy of Sciences
Wednesday, January 24, 2024, 14:30
- 16:00
Building 4, Level 5, Room 5220
In this short course, we will introduce some elements in deriving the hp a posteriori error estimate for a high-order unfitted finite element method for elliptic interface problems. The key ingredient is an hp domain inverse estimate, which allows us to prove a sharp lower bound of the hp a posteriori error estimator.
Erick Chacon Montalvan, Postdoctoral fellow, Statistics Geohealth Group, KAUST
Thursday, October 19, 2023, 12:00
- 13:00
Building 9, Level 2, Room 2325
Spatial data analysis commonly needs to deal with spatial data derived from multiple sources (e.g. satellites, stations, survey samples) with different supports, but associated with the same properties of a spatial phenomenon under interest. Usually, predictors are also measured on different spatial supports than the response variable.
Thursday, August 31, 2023, 12:00
- 13:00
Building 9, Level 2, Room 2325
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Estimating first-order intensity functions is crucial in the analysis of point patterns on linear networks, but selecting suitable bandwidths for non-parametric methods remains challenging. We propose an adaptive intensity estimator for the heating kernel that adjusts bandwidths based on data points, a novel approach in this context.