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Tue, 11. Nov at 11:15
1.023 (BMS Room, ...
Non-perturbative Schwinger-Dyson equation for DT/PT vertices
Abstract. Non-perturbative Schwinger-Dyson (SD) equation is a discrete analog of the loop equation appearing in matrix models and related models. It was initially discussed for the gauge theory partition function and, as in the case of the matrix models, it has a close relation to the underlying infinite-dimensional symmetry of the system. In this talk, I'd like to discuss the non-perturbative SD equation for the higher-dimensional gauge theory partition function, interpreted as the generating function of the DT and PT invariants (DT/PT vertices) for CY3 and CY4 varieties, and address its underlying quantum algebraic structure, which is a consequence of geometric representation theory of the corresponding algebra.
Tue, 11. Nov at 13:15
Humboldt-Universi...
A non-linear diffusion problem
Abstract. In this talk we consider a non-linear diffusion problem with a non-local diffusion coefficient. We explain how to transform this problem to a fixed point problem in either the set of real numbers or a linear Poisson problem has to be solved in each iteration.
Tue, 11. Nov at 13:15
Humboldt-Universi...
Wed, 12. Nov at 10:00
Weierstrass-Insti...
Demystifying Spatial Confounding
Abstract. Spatial confounding is a fundamental issue in spatial regression models which arises because spatial random effects, included to approximate unmeasured spatial variation, are typically not independent of covariates in the model. This can lead to significant bias in covariate effect estimates. The problem is complex and has been the topic of extensive research with sometimes puzzling and seemingly contradictory results. We will give an introduction to spatial confounding and discuss some suggested solutions for dealing with it, including the formalisation as a structural equation model and spatial+, where spatial variation in the covariate of interest is regresssed away first and remaining residuals are then used to identify the relevant effect. In the second part of the presentation, we develop a broad theoretical framework that brings mathematical clarity to the mechanisms of spatial confounding, relying on an explicit analytical expression for the resulting bias. We see that the problem is directly linked to spatial smoothing and identify exactly how the size and occurrence of bias relate to the features of the spatial model as well as the underlying confounding scenario. Using our results, we can explain subtle and counter-intuitive behaviours. Finally, we propose a general approach for dealing with spatial confounding bias in practice, applicable for any spatial model specification. When a covariate has non-spatial information, we show that a general form of the so-called spatial+ method can be used to eliminate bias. When no such information is present, the situation is more challenging but, under the assumption of unconfounded high frequencies, we develop a procedure in which multiple capped versions of spatial+ are applied to assess the bias in this case. We illustrate our approach with an application to air temperature in Germany.
Wed, 12. Nov at 11:30
Weierstrass Lectu...
Genealogies in multitype populations: branching processes and structured coalescents
Abstract. This talk focuses on the interplay between type and ancestry in two different multitype population models. In the first part, we briefly discuss the long-term behavior of critical multitype branching processes conditioned on survival, both with respect to the forward and the ancestral processes. Despite substantial differences in forward-time behavior and required techniques, their ancestral processes retain key structural similarities to the supercritical case. The main part of the talk then focuses on structured populations divided into $d$ colonies, where individuals migrate at rates proportional to a global scaling parameter $K$. We sample $N(K)$ individuals evenly across colonies and trace their ancestral lineages backward in time. Within each colony, coalescence occurs at a constant rate as in the Kingman coalescent. We encode the system's state as a $d$-dimensional vector of empirical measures, recording both current lineage locations and the colonies of their sampled descendants. Our focus is on how the sample size affects the asymptotic behavior of this process as $K \to \infty$ (representing fast migration), distinguishing two regimes: the critical-sampling regime ($N(K) \sim K$) and the large-sampling regime ($N(K) \gg K$). After suitable time-space rescaling, we prove convergence to $d$-dimensional coagulation equations in both sampling regimes. In the critical regime, the solution admits a representation via a multitype birth-death process; in the large-sample regime, via the entrance law of a multitype Feller diffusion.
Wed, 12. Nov at 13:15
Room: 3.007 John ...
Wed, 12. Nov at 15:15
WIAS, Erhard-Schm...
Γ-expansion of the Cahn--Hilliard functional with Dirichlet boundary conditions
Abstract
Wed, 12. Nov at 16:30
EN 058
The Ricci flow on infinite circle packings
Abstract. The Koebe–Andreev–Thurston Theorem asserts that every finite simple planar graph can be realized as the contact graph of a circle packing. Among the various proofs, one notable approach is the discrete Ricci flow method introduced by Chow and Luo. In this talk, we will introduce the Ricci flow on infinite triangulations of open surfaces, a framework that may provide new tools for addressing problems involving infinite circle packings. This is joint work with Huabin Ge and Puchun Zhou.
Thu, 13. Nov at 10:00
SR 009, Arnimallee 6
Small-scale and large-scale problems in function spaces
Thu, 13. Nov at 14:00
The adjoint degree and translation scissors congruence
Abstract. The adjoint polynomial of a convex polytope P entails many nice information on the geometry and combinatorics of P. It gives strong insights in the structure of the residual arrangement of P, that is, the hyperplane arrangement obtained by extending the facets. The adjoint appears, moreover, if one seeks to find "nice" convex combinations for interior points of P, or as the numerator of the canonical form of P. In this talk, we are concerned with the degree of the adjoint polynomial. In the projective setting, this degree is always m-d-1, where m is the number of facets of P. For non-generic polytopes in an affine space, the degree may be lower, which could be seen as a hint on parallelisms among the faces of P. Our main tool to study the degree drop of the adjoint is a modification of the so-called canonical form. That way we obtain a translation invariant valuation that vanishes exactly when the adjoint degree is "lower than usual". We will see that this function is, in addition, 1-homogeneous and therefore commutes with Minkowski addition. From these properties we derive structural results on polytopes for which the adjoint degree is low, including a full characterization of all possible degrees in dimension up to 3. This is a joint work with Tom Baumbach, Julian Weigert and Martin Winter.
Fri, 14. Nov
Tue, 18. Nov at 11:15
1.023 (BMS Room, ...
Wall-crossing, Schur indices and symmetric quivers
Abstract. I will show that symmetric quivers encode various observables of 4d N=2 theories related to wall-crossing phenomena. The observables in question include (wild) Donaldson-Thomas invariants, as well as Schur indices, which at the same time are known to reproduce characters of 2d conformal field theories. Furthermore, symmetric quivers of our interest encode 3d N=2 theories, therefore all these relations can be interpreted as a web of dualities between 2d, 3d, and 4d systems.
Tue, 18. Nov at 13:15
Humboldt-Universi...
Wed, 19. Nov at 10:00
Weierstrass-Insti...
Wed, 19. Nov at 13:00
Overparametrization as a Feature, Not a Bug: How Excess Capacity Drives Sparsity and Informs Uncertainty
Abstract. Overparametrized neural networks are often viewed as wasteful, yet they can be a powerful driver of structure and sparsity. This talk examines how excess capacity can be leveraged to induce sparse and interpretable representations. I will begin by showing how differentiable sparsity mechanisms transform redundant parameterizations into structured solutions with theoretical guarantees. Building on this, I will link these ideas to insights on balancedness and equal-probability manifolds in Bayesian neural networks, where overparametrization shapes the posterior geometry and promotes prior conformity. The talk concludes with an outlook on foundation models and their potential for scalable, uncertainty-aware learning.
Wed, 19. Nov at 15:15
WIAS, Erhard-Schm...
An H-convergence-based implicit function theorem and homogenization of nonlinear non-smooth elliptic systems
Abstract
Wed, 19. Nov at 16:00
Wed, 19. Nov at 16:30
EN 058
THE Lp-BRUNN-MINKOWSKI INEQUALITIES FOR VARIATIONAL FUNCTIONALS WITH 0 ≤ p < 1
Abstract
Thu, 20. Nov at 10:00
SR 009, Arnimallee 6
Stochastic differential mean field games
Thu, 20. Nov at 14:00
Thu, 20. Nov at 16:15
HU Berlin, Instit...
Continuous-time persuasion by filtering
Abstract. We frame dynamic persuasion in a partial observation stochastic control game with an ergodic criterion. The receiver controls the dynamics of a multidimensional unobserved state process. Information is provided to the receiver through a device designed by the sender that generates the observation process. The commitment of the sender is enforced and an exogenous information process outside the control of the sender is allowed. We develop this approach in the case where all dynamics are linear and the preferences of the receiver are linear-quadratic. We prove a verification theorem for the existence and uniqueness of the solution of the HJB equation satisfied by the receiver’s value function. An extension to the case of persuasion of a mean field of interacting receivers is also provided. We illustrate this approach in two applications: the provision of information to electricity consumers with a smart meter designed by an electricity producer; the information provided by carbon footprint accounting rules to companies engaged in a best-in-class emissions reduction effort. In the first application, we link the benefits of information provision to the mispricing of electricity production. In the latter, we show that when firms declare a high level of best-in-class target, the information provided by stringent accounting rules offsets the Nash equilibrium effect that leads firms to increase pollution to make their target easier to achieve. This is a joint work with Prof. René Aïd, Prof. Giorgia Callegaro and Prof. Luciano Campi.
Fri, 21. Nov at 14:15
TU (MA 042)
Anatomy of big algebras
Abstract
Mon, 24. Nov at 15:00
Rudower Chaussee ...
Strange formulas in homogenization theory
Mon, 24. Nov at 15:15
Rudower Chaussee ...
Tue, 25. Nov at 11:15
1.023 (BMS Room, ...
Panorama of matrix models and topological recursion I
Abstract. This is crash course which aims at explaning various aspects of: random matrix ensembles and Coulomb gases, loop equations, spectral curves, topological recursion, maps, free probability, how they fit together and pose some open problems.
Wed, 26. Nov at 10:00
Weierstrass-Insti...
Physics-informed Functional Principal Component Analysis of Large-scale Datasets
Abstract. Physics-informed statistical learning is an emerging area of spatial and functional data analysis that integrates observational data with prior physical knowledge encoded by partial differential equations (PDEs). We propose an iterative Majorization–Minimization scheme for functional Principal Component Analysis of random fields in a general Hilbert space, formulated under the practically relevant assumption of partial observability of the data. By combining differential penalties with finite element discretizations, our approach recovers smooth principal component functions while preserving the geometric features of the spatial domain. The resulting estimation procedure involves solving a smoothing problem, which may become computationally demanding for large-scale datasets. After establishing the well-posedness of this smoothing problem under mild assumptions on the PDE parameters, we develop an efficient iterative algorithm for its solution. This framework enables the practical analysis of massive functional datasets at the population level, ranging from physics-informed fPCA to functional clustering, with applications to environmental and neuroimaging data.
Wed, 26. Nov at 11:30
Weierstrass Lectu...
Wed, 26. Nov at 13:15
Room: 3.007 John ...
Wed, 26. Nov at 15:15
WIAS, Erhard-Schm...
Random boundary conditions for open resonators and the Laplace--Beltrami--Weyl asymptotics
Abstract
Thu, 27. Nov at 10:00
SR 009, Arnimallee 6
Ergodicity of φ^4_3
Fri, 28. Nov
Tue, 02. Dec at 11:15
1.023 (BMS Room, ...
Small deviations of Gaussian multiplicative chaos and the free energy of the two-dimensional massless Sinh-Gordon model
Abstract. We derive a bound on the probability that the total mass of Gaussian multiplicative chaos measure obtained from a Gaussian field with zero spatial average, is small. We also give the probabilistic path integral formulation of the massless Sinh-Gordon model on a torus of side length R, and study its partition function R tends to infinity. We apply the small deviation bounds for Gaussian multiplicative chaos to obtain lower and upper bounds for the logarithm of the partition function, leading to the existence of a non-zero and finite subsequential infinite volume limit for the free energy.
Wed, 03. Dec at 10:00
Weierstrass-Insti...
Wed, 03. Dec at 16:30
EN 058
Thu, 04. Dec at 10:00
SR 009, Arnimallee 6
Thu, 04. Dec at 10:00
SR 009, Arnimallee 6
Robust Filtering: Correlated Noise and Multidimensional Observation
Thu, 04. Dec at 16:15
HU Berlin, Instit...
Thu, 04. Dec at 17:15
HU Berlin, Instit...
Fri, 05. Dec
Fri, 05. Dec at 14:15
TU (MA 042)
Randomness, quasirandomness, and decomposition problems
Tue, 09. Dec at 11:15
1.023 (BMS Room, ...
Panorama of matrix models and topological recursion II
Abstract. This is crash course which aims at explaning various aspects of: random matrix ensembles and Coulomb gases, loop equations, spectral curves, topological recursion, maps, free probability, how they fit together and pose some open problems.
Wed, 10. Dec at 10:00
Weierstrass-Insti...
Thu, 11. Dec at 10:00
SR 009, Arnimallee 6
Anomalous diffusions in inhomogeneous media
Thu, 11. Dec at 10:00
SR 009, Arnimallee 6
Anomalous diffusions in inhomogeneous media
Fri, 12. Dec at 14:15
FU (T9)
Tue, 16. Dec at 11:15
1.023 (BMS Room, ...
Panorama of matrix models and topological recursion III
Abstract. This is crash course which aims at explaning various aspects of: random matrix ensembles and Coulomb gases, loop equations, spectral curves, topological recursion, maps, free probability, how they fit together and pose some open problems.
Wed, 17. Dec at 10:00
Weierstrass-Insti...
Incomplete U-Statistics of Equireplicate Designs: Berry-Esseen Bound and Efficient Construction.
Abstract. U-statistics are a fundamental class of estimators that generalize the sample mean and underpin much of nonparametric statistics. Although extensively studied in both statistics and probability, key challenges remain. These include their inherently high computational cost—addressed partly through incomplete U-statistics—and their non-standard asymptotic behavior in the degenerate case, which typically requires resampling methods for hypothesis testing. This talk presents a novel perspective on U-statistics, grounded in hypergraph theory and combinatorial designs. Our approach bypasses the traditional Hoeffding decomposition, which is the the main analytical tool in this literature but is highly sensitive to degeneracy. By fully characterizing the dependence structure of a U-statistic, we derive a new Berry–Esseen bound that applies to all incomplete U-statistics based on deterministic designs, yielding conditions under which Gaussian limiting distributions can be established even in the degenerate case and when the order diverges. Moreover, we introduce efficient algorithms to construct incomplete U-statistics of equireplicate designs, a subclass of deterministic designs that, in certain cases, enable to achieve minimum variance. To illustrate the power of this novel framework, we apply it to kernel-based testing, focusing on the widely used two-sample Maximum Mean Discrepancy (MMD) test. Our approach leads to a permutation-free variant of the MMD test that delivers substantial computational gains while retaining statistical validity.
Wed, 17. Dec at 15:15
WIAS, Erhard-Schm...
A weak-strong uniqueness principle for the Mullins--Sekerka equation
Abstract
Thu, 18. Dec at 16:15
HU Berlin, Instit...
Thu, 18. Dec at 17:15
HU Berlin, Instit...
Wed, 07. Jan at 13:15
Room: 3.007 John ...
Secant loci of scrolls over curves
Abstract. The secant loci associated to a linear system l over a curve C parametrise effective divisors which impose fewer conditions than expected on l. For a rank r bundle E and a space of global sections of E, we define and investigate generalised secant loci, which are determinantal loci on Quot schemes of torsion quotients of E. We extend the Abel-Jacobi map to the context of Quot schemes, and examine the relation between smoothness of generalised secant loci and their associated Brill-Noether loci. In one case, we indicate how formulas of Oprea-Pandharipande and Stark can be used to enumerate the generalised secant locus when it has and attains expected dimension zero.
Tue, 13. Jan at 11:15
1.023 (BMS Room, ...
Wed, 14. Jan at 10:00
Weierstrass-Insti...
Thu, 15. Jan at 17:15
HU Berlin, Instit...
Fri, 16. Jan
Thu, 22. Jan at 15:15
Rudower Chaussee ...
Fri, 23. Jan
Fri, 23. Jan
Tue, 27. Jan at 11:15
1.023 (BMS Room, ...
Wed, 28. Jan at 10:00
Weierstrass-Insti...
Thu, 29. Jan at 16:15
HU Berlin, Instit...
Thu, 29. Jan at 16:15
Equivalence between local and global Hadamard States with Robin boundary conditions on half-Minkowski spacetime
Abstract. We construct the fundamental solutions and Hadamard states for a Klein-Gordon field in half-Minkowski spacetime with Robin boundary conditions in arbitrary dimensions using a generalisation of the Robin-to-Dirichlet map. On the one hand this allows us to prove the uniqueness and support properties of the Green operators. On the other hand, we obtain a local representation for the Hadamard parametrix that provides the correct local definition of Hadamard states, capturing 'reflected' singularities from the spacetime timelike boundary. This allows us to prove the equivalence of our local Hadamard condition and the global Hadamard condition with a wave-front set described in terms of generalized broken bicharacteristics, obtaining a Radzikowski-like theorem in half-Minkowski spacetime.<br>Joint work with B. Costeri, R. D. Singh and B. Juárez-Aubry -- ArXiv: 2509.26035 [math-ph]
Thu, 29. Jan at 17:15
HU Berlin, Instit...
Fri, 30. Jan
Wed, 11. Feb at 10:00
Weierstrass-Insti...
Linear Monge is All You Need
Abstract. In this talk, we explore the geometry of the Bures-Wasserstein space for potentially degenerate Gaussian measures on a separable Hilbert space, based on our recent work with Yoav Zemel. A key feature of our approach is its simplicity: relying only on elementary arguments from linear operator theory, we are able to derive explicit results without resorting to Kantorovich duality or Otto's Calculus. We provide a complete characterisation of both the Monge and Kantorovich problems in this context, regardless of the degeneracy of their measures. Furthermore, we show a simple way to construct all possible Wasserstein geodesics connecting two Gaussian measures. Finally, we generalise our results to characterise Wasserstein barycenters of Gaussian measures, borrowing the idea of Procrustes distance from statistical shape analysis
Wed, 11. Feb at 10:00
Weierstrass-Insti...
Kernel ridge regression for spherical responses
Abstract. The aim is to propose a novel nonlinear regression framework for responses taking values on a hypersphere. Rather than performing tangent space regression, where all the sphere responses are lifted to a single tangent space on which the regression is performed, we estimate conditional Frechet means by minimizing squared distances on the nonlinear manifold. Yet, the tangent space serves as a linear predictor space where the regression function takes values. The framework integrates Riemannian geometry techniques with functional data analysis by modelling the regression function using methods from vector-valued reproducing kernel Hilbert space theory. This formulation enables the reduction of the infinite-dimensional estimation problem to a finite-dimensional one via a representer theorem and leads to an estimation algorithm by means of Riemannian gradient descent. Explicit checkable conditions on the data that ensure the existence and uniqueness of the minimizing estimator are given.
Wed, 11. Feb at 15:15
WIAS, Erhard-Schm...
Thu, 12. Feb at 16:15
HU Berlin, Instit...
Thu, 12. Feb at 17:15
HU Berlin, Instit...