Tootfinder

Additive Distributionally Robust Ranking and Selection
Zaile Li, Yuchen Wan, L. Jeff Hong
https://arxiv.org/abs/2509.06147 https://arxiv.org/pdf/2509.06147…

Additive Distributionally Robust Ranking and Selection
Ranking and selection (R&S) aims to identify the alternative with the best mean performance among $k$ simulated alternatives. The practical value of R&S depends on accurate simulation input modeling, which often suffers from the curse of input uncertainty due to limited data. Distributionally robust ranking and selection (DRR&S) addresses this challenge by modeling input uncertainty via an ambiguity set of $m > 1$ plausible input distributions, resulting in $km$ scenarios in total. Recent DRR&S…

Modelling and Simulation of an Alkaline Ni/Zn Cell
Felix K. Schwab, Britta Doppl, Niklas J. Herrmann, Alice Boudet, Shadi Mirhashemi, Sylvain Brimaud, Birger Horstmann
https://arxiv.org/abs/2509.05026 …

Modelling and Simulation of an Alkaline Ni/Zn Cell
Nickel/zinc (Ni/Zn) technology is a promising post-lithium battery type for stationary applications with respect to aspects such as safety, environmental compatibility and resource availability. Although this battery type has been known for a long time, the theoretical knowledge about the processes taking place in the battery is limited. In order to gain a deeper understanding of the general cycling behaviour and the underlying processes, but also specific phenomena intrinsic to zinc-based cell…

Case Studies of Generative Machine Learning Models for Dynamical Systems
Nachiket U. Bapat, Randy C. Paffenroth, Raghvendra V. Cowlagi
https://arxiv.org/abs/2508.04459 https://

Case Studies of Generative Machine Learning Models for Dynamical Systems
Systems like aircraft and spacecraft are expensive to operate in the real world. The design, validation, and testing for such systems therefore relies on a combination of mathematical modeling, abundant numerical simulations, and a relatively small set of real-world experiments. Due to modeling errors, simplifications, and uncertainties, the data synthesized by simulation models often does not match data from the system's real-world operation. We consider the broad research question of whether …

Convolutional autoencoders for the reconstruction of three-dimensional interfacial multiphase flows
Murray Cutforth, Shahab Mirjalili
https://arxiv.org/abs/2508.04084 https://…

Convolutional autoencoders for the reconstruction of three-dimensional interfacial multiphase flows
In this work, we perform a comprehensive investigation of autoencoders for reduced-order modeling of three-dimensional multiphase flows. Focusing on the accuracy of reconstructing multiphase flow volume/mass fractions with a standard convolutional architecture, we examine the advantages and disadvantages of different interface representation choices (diffuse, sharp, level set). We use a combination of synthetic data with non-trivial interface topologies and high-resolution simulation data of mu…

UniVerse: Unleashing the Scene Prior of Video Diffusion Models for Robust Radiance Field Reconstruction
Jin Cao, Hongrui Wu, Ziyong Feng, Hujun Bao, Xiaowei Zhou, Sida Peng
https://arxiv.org/abs/2510.01669

UniVerse: Unleashing the Scene Prior of Video Diffusion Models for Robust Radiance Field Reconstruction
This paper tackles the challenge of robust reconstruction, i.e., the task of reconstructing a 3D scene from a set of inconsistent multi-view images. Some recent works have attempted to simultaneously remove image inconsistencies and perform reconstruction by integrating image degradation modeling into neural 3D scene representations.However, these methods rely heavily on dense observations for robustly optimizing model parameters.To address this issue, we propose to decouple robust reconstructi…

Pressure dependence of magnetron sputtering: 2D-RZ particle-in-cell and 1D fluid modeling
Joseph G. Theis (University of Colorado Boulder), Gregory R. Werner (University of Colorado Boulder), Thomas G. Jenkins (Silvaco Inc), Daniel Main (Silvaco Inc), John R. Cary (University of Colorado Boulder, Silvaco Inc)
https://arxiv.org/abs/2509.036…

Pressure dependence of magnetron sputtering: 2D-RZ particle-in-cell and 1D fluid modeling
We reproduce the consistently-seen experimental voltage versus pressure (V-P) dependence of DC magnetron sputtering (DCMS) with 2D-RZ particle-in-cell (PIC) simulation. Informed by PIC simulation, we develop a steady-state, 1D-axial fluid model of the sheath and presheath that also reproduces this V-P dependence. The V-P dependence is the relationship between the steady-state voltage needed to maintain a constant discharge current and the neutral gas pressure. V-P dependence is fundamental to d…

Design, Simulation, and Fabrication of a Hexagonal Microfluidic Platform for Culturing Neurons
Maxx Yung
https://arxiv.org/abs/2508.00425 https://arxiv.org…

Design, Simulation, and Fabrication of a Hexagonal Microfluidic Platform for Culturing Neurons
Developing an organoid computing platform from neurons in vitro demands stable, precisely controlled microenvironments. To address this requirement, we designed, simulated, and fabricated a microfluidic device featuring hexagonal wells ($34.64\,\mathrm{μm}$ side length) in a honeycomb array connected by $20\,\mathrm{μm}$ channels. Computational fluid dynamics (CFD) modeling, validated by high mesh quality ($0.934$ orthogonal quality) and robust convergence, confirmed the architecture supports…

Impact of Uncertainties in Spectral Energy Distribution Modelling on Inferred Galaxy Properties
Gareth T. Jones, Conor M. Byrne, Elizabeth R. Stanway
https://arxiv.org/abs/2509.02741

Impact of Uncertainties in Spectral Energy Distribution Modelling on Inferred Galaxy Properties
Interpreting galaxy properties from astronomical surveys relies heavily on spectral energy distribution (SED) modelling, yet uncertainties in key model ingredients are often overlooked. By leveraging a $z\sim0$ galaxy sample from the EAGLE simulation, we generate synthetic SDSS spectral and VISTA photometric observations with controlled assumptions, to assess how variations in stellar spectral library, initial mass function (IMF) and metallicity prescriptions within the BPASS-framework affect i…

MicroLabVR: Interactive 3D Visualization of Simulated Spatiotemporal Microbiome Data in Virtual Reality
Simon Burbach, Maria Maleshkova, Florian Centler, Tanja Joan Schmidt
https://arxiv.org/abs/2508.21736

MicroLabVR: Interactive 3D Visualization of Simulated Spatiotemporal Microbiome Data in Virtual Reality
Microbiomes are a vital part of the human body, engaging in tasks like food digestion and immune defense. Their structure and function must be understood in order to promote host health and facilitate swift recovery during disease. Due to the difficulties in experimentally studying these systems in situ, more research is being conducted in the field of mathematical modeling. Visualizing spatiotemporal data is challenging, and current tools that simulate microbial communities' spatial and tempor…

Fast Computation of Path Integrals of Killed Processes Using Confined Stochastic Bridges
Henrique B. N. Monteiro, Daniel M. Tartakovsky
https://arxiv.org/abs/2508.03671 https://…

Fast Computation of Path Integrals of Killed Processes Using Confined Stochastic Bridges
Expectations of path integrals of killed stochastic processes play a central role in several applications across physics, chemistry, and finance. Simulation-based evaluation of these functionals is often biased and numerically expensive due to the need to explicitly approximate stochastic paths and the challenge of correctly modeling them in the neighborhood of the killing boundary. We consider Itô processes killed at the boundary of some set in the $n$-dimensional space and introduce a novel …