Prof. Francesca Gardini,
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.
Thursday, March 07, 2024, 12:00
- 13:00
B9, L2, R2325, H2
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When pressure acoustic waves interact with rotating scatterers, they undergo peculiar and intriguing characteristics. In this talk, I will discuss our recent findings on the physics of acoustic scattering and propagation in spinning fluids.
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.
Soufiane Hayou, Postdoc, Simons Institute, UC Berkeley
Monday, February 26, 2024, 09:00
- 10:00
Building 9, Level 4, Room 4225
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Neural networks have achieved impressive performance in many applications such as image and speech recognition and generation. State-of-the-art performance is usually achieved via a series of engineered modifications to existing neural architectures and their training procedures. However, a common feature of these systems is their large-scale nature: modern neural networks usually contain Billions - if not 10's of Billions - of trainable parameters, and empirical evaluations (generally) support the claim that increasing the scale of neural networks (e.g. width and depth) boosts the model performance if done correctly. However, given a neural network model, it is not straightforward to address the crucial question `how do we scale the network?'. In this talk, I will show how we can leverage different mathematical results to efficiently scale neural networks, with empirically confirmed benefits.
Thursday, February 15, 2024, 12:00
- 13:00
Building 9, Level 2, Room 2325, Hall 2
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Time series clustering is an essential machine learning task with applications in many disciplines. While the majority of the methods focus on time series taking values on the real line, very few works consider time series defined on the unit circle, although the latter objects frequently arise in many applications. In this talk, the problem of clustering circular time series is discussed.
Thursday, February 08, 2024, 12:00
- 13:10
Building 9, Level 2, Room 2325, Hall 2
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Brain activity over the entire network is complex. A full understanding of brain activity requires careful study of its multi-scale spatial-temporal organization. Motivated by these challenges, we will explore some characterizations of dependence between components of a multivariate time series and then apply these to the study of brain functional connectivity.
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.
Thursday, January 25, 2024, 12:00
- 13:00
Building 9, Level 2, Room 2325, Hall 2
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Partial differential equations are a mathematical tool widely used to model phenomena in several different fields. A stochastic partial differential equation (SPDE) introduces random forcing to take the nature of real-world observations.
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.
Thursday, December 07, 2023, 12:00
- 13:00
Building 9, Level 2, Room 2325
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In this talk, we consider Bayesian parameter inference associated to a class of partially observed stochastic differential equations (SDE) driven by jump processes. Such type of models can be routinely found in applications, of which we focus on the case of neuroscience.
Thursday, November 30, 2023, 12:00
- 13:00
Building 9, Level 2, Room 2325
Many problems in applied geometry amount to the solution of a typically nonlinear partial differential equation. We will discuss why it may not be a good idea to discretize the equation, but to take the viewpoint of discrete differential geometry and discretize the theory.
Thursday, November 23, 2023, 12:00
- 13:00
Building 9, Level 2, Room 2325
Contact Person
Clinical research often requires the simultaneous study of longitudinal repeated measurements and time-to-event (i.e., survival) data. Joint models, which can combine these two types of data, are invaluable tools in this context.
Thursday, November 16, 2023, 12:00
- 13:00
Building 9, Level 2, Room 2325
We are all familiar with the tendency of water waves to break in shallow water, for instance at the beach. Indeed, breaking is a universal behavior of solutions to first-order nonlinear hyperbolic PDEs, and manifests itself in phenomena ranging from traffic jams to shock waves.
Tuesday, November 14, 2023, 09:00
- 17:00
Building 1, Level 4, seaside, Room 4102
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This workshop will cover the statistical essentials to designing experiments including power analysis, sample size calculation, regression, and variance analysis. A statistically sound experimental design is essential in applying for research grants. Detailed syllabi will be sent to those who register. Limited seats. Sign up now!
Prof. Narayanaswamy Balakrishnan, Department of Mathematics and Statistics, McMaster University
Sunday, November 12, 2023, 15:30
- 16:30
Building 1, Level 4, Room 4102
Contact Person
In this talk, I will describe the family of mean-mixtures of multivariate normal distributions and establish many of its properties, stochastic representations, moments, distributional shape characteristics, etc.
Sunday, November 12, 2023, 12:30
- 14:30
Building 5, Level 5, Room 5209
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The multivariate Gaussian distribution is widely used in many statistical applications due to its appealing features. However, real-world data often violate its assumptions, showing skewness and/or tail-thickness.
Thursday, November 02, 2023, 12:00
- 13:00
Building 9, Level 2, Room 2325
Free boundary problems arise naturally in a range of mathematical models that describe physical, biological or financial phenomena, such as the melting of ice into water, the dynamics of a population or the behavior of stock markets, to mention just a few.
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, October 12, 2023, 12:00
- 13:00
Building 9, Level 2, Room 2325
In this talk we propose and validate a Space Multiscale model for the description of particle diffusion in the presence of trapping boundaries. We start from a drift diffusion equation in which the drift term describes the effect of bubble traps, and it is simulated by the Lennard–Jones potential.
Thursday, September 28, 2023, 12:00
- 13:00
Building 9, Level 2, Room 2325
We study theoretical problems of fault diagnosis in circuits and switching networks, which are among the most fundamental models for computing Boolean functions.