Tootfinder

A Systematic Particle Filter for Estimating Time-Varying Parameters in Advection-Diffusion Equations with Source Terms
Andrea Arnold
https://arxiv.org/abs/2508.12155 https://

A Systematic Particle Filter for Estimating Time-Varying Parameters in Advection-Diffusion Equations with Source Terms
Many real-world systems modeled using partial differential equations (PDEs) involve unknown parameters that must be estimated from limited, noisy system observations. While typically assumed to be constants, some of these unobserved parameters may vary with time. This work proposes a two-phase, offline-online numerical procedure for systematically estimating and quantifying uncertainty in time-varying parameters (TVPs) in time-dependent PDEs, specifically focusing on advection-diffusion models …

Deadbeat Robust Model Predictive Control for Linear Parameter-Varying Systems
Georg Schildbach, Hossam S. Abbas
https://arxiv.org/abs/2509.14885 https://ar…

Deadbeat Robust Model Predictive Control for Linear Parameter-Varying Systems
The concept of Deadbeat Robust Model Predictive Control (DRMPC) is to completely extinguish the effect of external disturbances within the first few steps of the prediction horizon. The benefit is that the remaining dynamics of the system can be planned with certainty. This means it is not necessary to employ a Robust Positive Invariant (RPI) set as a terminal condition, which is often complex or even intractable to compute. Recent work has shown that it is possible to obtain full system theore…

From ANN to BNN: Inferring Reionization Parameters using Uncertainty-aware Emulators of 21-cm Summaries
Yashrajsinh Mahida, Sanjay Kumar Yadav, Suman Majumdar, Leon Noble, Chandra Shekhar Murmu, Saswata Dasgupta, Sohini Dutta, Himanshu Tiwari, Abinash Kumar Shaw
https://arxiv.org/abs/2508.13261

From ANN to BNN: Inferring Reionization Parameters using Uncertainty-aware Emulators of 21-cm Summaries
Inferring astrophysical parameters from radio interferometric observations of the redshifted 21-cm signal from the Epoch of Reionization (EoR) is a challenging yet crucial task. The 21-cm signal from EoR is expected to be highly non-Gaussian; therefore, we need to use higher-order statistics, e.g., bispectrum. Moreover, the forward modeling of the signal and its statistics for a varying set of model parameters requires rerunning the simulations many times, which is computationally very expensiv…

Sub-Poisson distributions: Concentration inequalities, optimal variance proxies, and closure properties
Lasse Leskel\"a, Ian V\"alimaa
https://arxiv.org/abs/2508.12103

Sub-Poisson distributions: Concentration inequalities, optimal variance proxies, and closure properties
We introduce a nonasymptotic framework for sub-Poisson distributions with moment generating function dominated by that of a Poisson distribution. At its core is a new notion of optimal sub-Poisson variance proxy, analogous to the variance parameter in the sub-Gaussian setting. This framework allows us to derive a Bennett-type concentration inequality without boundedness assumptions and to show that the sub-Poisson property is closed under key operations including independent sums and convex com…

Direct data-driven interpolation and approximation of linear parameter-varying system trajectories
Chris Verhoek, Ivan Markovsky, Roland T\'oth
https://arxiv.org/abs/2508.11332

Direct data-driven interpolation and approximation of linear parameter-varying system trajectories
We consider the problem of estimating missing values in trajectories of linear parameter-varying (LPV) systems. We solve this interpolation problem for the class of shifted-affine LPV systems. Conditions for the existence and uniqueness of solutions are given and a direct data-driven algorithm for its computation is presented, i.e., the data-generating system is not given by a parametric model but is implicitly specified by data. We illustrate the applicability of the proposed solution on illus…

Categorical Policies: Multimodal Policy Learning and Exploration in Continuous Control
SM Mazharul Islam, Manfred Huber
https://arxiv.org/abs/2508.13922 https://

Categorical Policies: Multimodal Policy Learning and Exploration in Continuous Control
A policy in deep reinforcement learning (RL), either deterministic or stochastic, is commonly parameterized as a Gaussian distribution alone, limiting the learned behavior to be unimodal. However, the nature of many practical decision-making problems favors a multimodal policy that facilitates robust exploration of the environment and thus to address learning challenges arising from sparse rewards, complex dynamics, or the need for strategic adaptation to varying contexts. This issue is exacerb…

Extended Parameter Shift Rules with Minimal Derivative Variance for Parameterized Quantum Circuits
Zhijian Lai, Jiang Hu, Dong An, Zaiwen Wen
https://arxiv.org/abs/2508.08802 ht…

Extended Parameter Shift Rules with Minimal Derivative Variance for Parameterized Quantum Circuits
Parameter shift rules (PSRs) are useful methods for computing arbitrary-order derivatives of the cost function in parameterized quantum circuits. The basic idea of PSRs is to evaluate the cost function at different parameter shifts, then use specific coefficients to combine them linearly to obtain the exact derivatives. In this work, we propose an extended parameter shift rule (EPSR) which generalizes a broad range of existing PSRs and has the following two advantages. First, EPSR offers an inf…

Strong overlap of deterministic and stochastic dynamics in a super-diffusive regime
Muhammad Tayyab, Jahanzeb Tariq
https://arxiv.org/abs/2508.12091 https://

Strong overlap of deterministic and stochastic dynamics in a super-diffusive regime
We consider deterministic dynamics, known as the slicer map (SM), which exhibits normal and anomalous diffusion by varying a single parameter. The statistics of the position moments and the low-order position autocorrelation function (PACF) of the SM closely overlap with those of a stochastic process called the Lévy-Lorentz gas (LLg), particularly in the normal and strongly superdiffusive anomalous regimes. However, matching low-order statistics alone cannot fully characterize the microscopic …

Estimation in linear models with clustered data
Anna Mikusheva, Mikkel S{\o}lvsten, Baiyun Jing
https://arxiv.org/abs/2508.12860 https://arxiv.org/pdf/2508…

Estimation in linear models with clustered data
We study linear regression models with clustered data, high-dimensional controls, and a complicated structure of exclusion restrictions. We propose a correctly centered internal IV estimator that accommodates a variety of exclusion restrictions and permits within-cluster dependence. The estimator has a simple leave-out interpretation and remains computationally tractable. We derive a central limit theorem for its quadratic form and propose a robust variance estimator. We also develop inference …

Gravitational Lensing by Black Holes in Einstein-nonlinear Electrodynamic Theories with Multiple Photon Spheres
Siyuan Hui, Benrong Mu, Peng Wang
https://arxiv.org/abs/2507.13048 …

Gravitational Lensing by Black Holes in Einstein-nonlinear Electrodynamic Theories with Multiple Photon Spheres
In this paper, we study the gravitational lensing effects of non-linear electrodynamic black holes. Non-linear electrodynamic black holes serve as typical models for multi-event horizon black holes. Depending on the choice of metric parameters, these black holes can possess more than five event horizons. Consequently, within certain parameter ranges, black holes can have more than three photon spheres of varying sizes outside the event horizon. Specifically, we focus on the strong gravitational…