Uploaded March 2024 | Updated September 2026, 2 weeks ago
Anders Björn - The Dirichlet Problem and Boundary Regularity: How To Attain the Boundary Values in a Good Way (TSVP Talk at OIST)
Anders Björn is visiting OIST from 2024-01-08 until 2024-08-15 through the "Theoretical Sciences Visiting Program" (TSVP). Find out more about the TSVP on the program website:
oist.jp/visiting-program.
Abstract: In the classical Dirichlet boundary value problem one seeks a solution to some differential equation so that it takes prescribed boundary values. I will first focus on the Dirichlet problem for harmonic functions, and explain why it is not always solvable so that the boundary data are taken pointwise. A natural question is then how to relax the problem so that it always has a reasonable solution. This will lead to a discussion of so-called regular and irregular boundary points, where the boundary data are or are not attained as limits. I will then turn to the corresponding nonlinear theory for p-harmonic functions on Rn and metric spaces.
Profile: Anders Björn is a Professor of Mathematics at Linköping University, Sweden, where he also received his PhD in 1995. He was a postdoc at University of Michigan, Ann Arbor, and has later spent longer research periods at Charles University in Prague, University of Cincinnati and the Mittag-Leffler Institute in Stockholm. His research is in analysis on metric spaces, mainly in collaboration with Jana Björn. In particular, he is interested in p-harmonic functions, partial differential equations and various minimization problems. He studies various solving methods and properties of the solutions, such as their interior and boundary regularity and growth estimates. This is research in pure mathematics and its aim is to provide rigorous fundamentals for and a better understanding of some problems, which could come from natural sciences and other fields. Analysis on metric spaces makes it possible to study such questions simultaneously in many different settings, for example on very rough sets and for highly nonhomogeneous media. It also brings new insight into which properties and assumptions are really essential for the theory and which are the main obstructions. A popular-scientific description of Anders's research is at this website.
#OIST #OIST_TSVP #Mathematics #DirichletProblem #BoundaryRegularity #MetricSpaces #Theoretical #Science #VisitingProgram #Okinawa #TSVP
Anders Björn - The Dirichlet Problem and Boundary Regularity: How To Attain the Boundary Values in a Good Way (TSVP Talk at OIST)
Anders Björn is visiting OIST from 2024-01-08 until 2024-08-15 through the "Theoretical Sciences Visiting Program" (TSVP). Find out more about the TSVP on the program website:
oist.jp/visiting-program.
Abstract: In the classical Dirichlet boundary value problem one seeks a solution to some differential equation so that it takes prescribed boundary values. I will first focus on the Dirichlet problem for harmonic functions, and explain why it is not always solvable so that the boundary data are taken pointwise. A natural question is then how to relax the problem so that it always has a reasonable solution. This will lead to a discussion of so-called regular and irregular boundary points, where the boundary data are or are not attained as limits. I will then turn to the corresponding nonlinear theory for p-harmonic functions on Rn and metric spaces.
Profile: Anders Björn is a Professor of Mathematics at Linköping University, Sweden, where he also received his PhD in 1995. He was a postdoc at University of Michigan, Ann Arbor, and has later spent longer research periods at Charles University in Prague, University of Cincinnati and the Mittag-Leffler Institute in Stockholm. His research is in analysis on metric spaces, mainly in collaboration with Jana Björn. In particular, he is interested in p-harmonic functions, partial differential equations and various minimization problems. He studies various solving methods and properties of the solutions, such as their interior and boundary regularity and growth estimates. This is research in pure mathematics and its aim is to provide rigorous fundamentals for and a better understanding of some problems, which could come from natural sciences and other fields. Analysis on metric spaces makes it possible to study such questions simultaneously in many different settings, for example on very rough sets and for highly nonhomogeneous media. It also brings new insight into which properties and assumptions are really essential for the theory and which are the main obstructions. A popular-scientific description of Anders's research is at this website.
#OIST #OIST_TSVP #Mathematics #DirichletProblem #BoundaryRegularity #MetricSpaces #Theoretical #Science #VisitingProgram #Okinawa #TSVP



![Florian Yger: A Geometric Adventure in Machine Learning (TSVP Talk at OIST)
[Title]
Florian Yger - A Geometric Adventure in Machine Learning: Learning with Invariances, Structures, and Prior Knowledge (TSVP Talk at OIST)
[Description]
Florian Yger is visiting OIST from 2024-05-20 until 2024-08-15 through the Theoretical Sciences Visiting Program (TSVP). Find out more about the TSVP on the program website:
https://www.oist.jp/visiting-program.
Abstract: In this presentation, we embark on a journey through the landscapes of machine learning, focusing on the role of representation learning. Traditional approaches often restrict themselves to Euclidean spaces, yet many real-world data, such as graphs and covariances, defy this simplistic framework. We explore how embracing non-Euclidean geometries—specifically curved spaces like Riemannian manifolds—unlocks new possibilities in understanding and predicting structured data. This framework allows for the incorporation of prior knowledge such as constraints and invariances into machine learning algorithms.
Central to our exploration is the Fréchet averaging problem, a fundamental tool that generalizes the well-know average to metric spaces. From this cornerstone, we derive many extensions ranging from graph averaging to dimensionality reduction on Riemannian manifolds. We illustrate their practical implications through numerical experiments on biomedical data.
Motivated by challenging applications, this presentation not only highlights the limitations of Euclidean-centric approaches but also underscores the potential of geometry-aware representation learning. Join us as we navigate this geometric adventure into the curved realm of geometry-aware representation learning.
Profile: Florian Yger is an associate professor at Université Paris-Dauphine since 2015, teaching Data Analysis and Machine Learning in the department MIDO. Within the LAMSADE, he is part of the team MILES which focuses on trustworthy Machine Learning and Explainable AI. From 2014 to 2015, he was a JSPS postdoctoral fellow in the laboratory of Prof. Sugiyama at Tokyo University. He received his PhD in Computer science from LITIS, Université de Rouen under the supervision of Alain Rakotomamonjy in 2013. He contributes to the problem of representation learning with a particular interest in the representation of structured data (graphs, covariance matrices,…) and the development of learning algorithm for non-Euclidean spaces. This work has many applications ranging from signal processing (EEG signals and Brain Computer Interface) and to image processing (Paintings for art style recognition). More recently, within MILES teams, he addresses the questions of trust, explainability and interpretability in machine learning models with a focus counterfactual reasoning on data. He recently started studying the interplay between computational social choice and machine learning in the context of voter’s opinion aggregation. He is a visiting researcher at RIKEN AIP, Japan since 2017, is a member of the Prairie (PaRis Artificial Intelligence Research InstitutE) where he holds a junior chair, and an Affiliate of the Theoretical Sciences Visiting Program (TSVP) at OIST.
#OIST #OIST_TSVP #MachineLearning #Geometry #Manifolds #Theoretical #Science #VisitingProgram #Okinawa #TSVP Florian Yger: A Geometric Adventure in Machine Learning (TSVP Talk at OIST)](https://i.ytimg.com/vi/KlU1mS1xr3I/mqdefault.jpg)






