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We introduce a general framework of low regularity integrators which allows us to approximate the time dynamics of a large class of equations, including parabolic and hyperbolic problems, as well as dispersive equations, up to arbitrary…

Numerical Analysis · Mathematics 2022-03-10 Yvonne Alama Bronsard , Yvain Bruned , Katharina Schratz

We introduce a numerical framework for dispersive equations embedding their underlying resonance structure into the discretisation. This will allow us to resolve the nonlinear oscillations of the PDE and to approximate with high order…

Numerical Analysis · Mathematics 2023-10-24 Yvain Bruned , Katharina Schratz

We introduce a new class of numerical schemes which allow for low regularity approximations to the expectation $ \mathbb{E}(|u_{k}(\tau, v^{\eta})|^2)$, where $u_k$ denotes the $k$-th Fourier coefficient of the solution $u$ of the…

Probability · Mathematics 2026-01-27 Yvonne Alama Bronsard , Yvain Bruned , Katharina Schratz

In this work, we propose a systematic derivation of resonance-based schemes via normal forms. The main idea is to use an arborification map on decorated trees together with a Butcher-Connes-Kreimer type coproduct and lower-dominant parts…

Numerical Analysis · Mathematics 2025-11-12 Yvain Bruned

A large toolbox of numerical schemes for dispersive equations has been established, based on different discretization techniques such as discretizing the variation-of-constants formula (e.g., exponential integrators) or splitting the full…

Numerical Analysis · Mathematics 2024-05-20 Frédéric Rousset , Katharina Schratz

Diffusion models are typically trained using pointwise reconstruction objectives that are agnostic to the spectral and multi-scale structure of natural signals. We propose a loss-level spectral regularization framework that augments…

Machine Learning · Computer Science 2026-03-04 Satish Chandran , Nicolas Roque dos Santos , Yunshu Wu , Greg Ver Steeg , Evangelos Papalexakis

The purpose of this paper is to build an algebraic framework suited to regularise branched structures emanating from rooted forests and which encodes the locality principle. This is achieved by means of the universal properties in the…

Mathematical Physics · Physics 2020-02-11 Pierre Clavier , Li Guo , Sylvie Paycha , Bin Zhang

We propose a new formalism for quantum field theory which is neither based on functional integrals, nor on Feynman graphs, but on marked trees. This formalism is constructive, i.e. it computes correlation functions through convergent rather…

High Energy Physics - Theory · Physics 2015-05-13 R. Gurau , J. Magnen , V. Rivasseau

We propose a principled method for autoencoding with random forests. Our strategy builds on foundational results from nonparametric statistics and spectral graph theory to learn a low-dimensional embedding of the model that optimally…

Machine Learning · Statistics 2026-01-16 Binh Duc Vu , Jan Kapar , Marvin Wright , David S. Watson

Using the methods of the theory of formal symmetries, we obtain new easily verifiable sufficient conditions for a recursion operator to produce a hierarchy of local generalized symmetries. An important advantage of our approach is that…

Exactly Solvable and Integrable Systems · Physics 2007-05-23 Artur Sergyeyev

Recent years have seen an increasing amount of research devoted to the development of so-called resonance-based methods for dispersive nonlinear partial differential equations. In many situations, this new class of methods allows for…

Numerical Analysis · Mathematics 2024-07-22 Georg Maierhofer , Katharina Schratz

Recently, sparsity-based algorithms are proposed for super-resolution spectrum estimation. However, to achieve adequately high resolution in real-world signal analysis, the dictionary atoms have to be close to each other in frequency,…

Machine Learning · Statistics 2015-06-05 Yiyuan She , Huanghuang Li , Jiangping Wang , Dapeng Wu

This paper derives a unifying theorem establishing consistency results for a broad class of tree-based algorithms. It improves current results in two aspects. First of all, it can be applied to algorithms that vary from traditional Random…

Statistics Theory · Mathematics 2024-02-22 Ricardo Blum , Munir Hiabu , Enno Mammen , Joseph T. Meyer

The wealth of data being gathered about humans and their surroundings drives new machine learning applications in various fields. Consequently, more and more often, classifiers are trained using not only numerical data but also complex data…

Machine Learning · Computer Science 2022-04-13 Maciej Piernik , Dariusz Brzezinski , Pawel Zawadzki

Resonance based numerical schemes are those in which cancellations in the oscillatory components of the equation are taken advantage of in order to reduce the regularity required of the initial data to achieve a particular order of error…

Numerical Analysis · Mathematics 2024-02-14 Jacob Armstrong-Goodall , Yvain Bruned

Within the context of Supersymmetric Quantum Mechanics and its related hierarchies of integrable quantum Hamiltonians and potentials, a general programme is outlined and applied to its first two simplest illustrations. Going beyond the…

Mathematical Physics · Physics 2015-06-11 Daddy Balondo Iyela , Jan Govaerts , M. Norbert Hounkonnou

The computation of time dynamics arising in nonlinear time-dependent partial differential equations is an ongoing challenge in numerical analysis, especially once roughness comes into play. Classical numerical schemes in general fail to…

Numerical Analysis · Mathematics 2025-04-29 Yvain Bruned , Frédéric Rousset , Katharina Schratz

We construct renormalised models of regularity structures by using a recursive formulation for the structure group and for the renormalisation group. This construction covers all the examples of singular SPDEs which have been treated so far…

Probability · Mathematics 2023-10-24 Yvain Bruned

We derive simple formulae for the transmittance $T$ and reflectance $R$ of Gaussian-Schell beams incident upon any stratified dielectric structure by using second-order classical coherence theory in the space-frequency picture. The…

Optics · Physics 2021-02-24 Paulo A. Brandão , João P. Mendonça , S. B. Cavalcanti

In this paper we study asymptotic properties of random forests within the framework of nonlinear time series modeling. While random forests have been successfully applied in various fields, the theoretical justification has not been…

Machine Learning · Statistics 2020-08-07 Richard A. Davis , Mikkel S. Nielsen
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