Tootfinder

Modeling and Simulation of Coupled Biochemical and Two-Phase Compositional Flow in Underground Hydrogen Storage
Elyes Ahmed, Brahim Amaziane, Salaheddine Chabab, St\'ephanie Delage Santacreu, Guillaume Galli\'ero, Olav M{\o}yner, Xavier Raynaud
https://arxiv.org/abs/2506.02582

Modeling and Simulation of Coupled Biochemical and Two-Phase Compositional Flow in Underground Hydrogen Storage
Integrating microbial activity into underground hydrogen storage models is crucial for simulating long-term reservoir behavior. In this work, we present a coupled framework that incorporates bio-geochemical reactions and compositional flow models within the Matlab Reservoir Simulation Toolbox (MRST). Microbial growth and decay are modeled using a double Monod formulation, with populations influenced by hydrogen and carbon dioxide availability. First, a refined Equation of State (EoS) is employe…

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…

This https://arxiv.org/abs/2505.19669 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Zero-Shot Streaming Text to Speech Synthesis with Transducer and Auto-Regressive Modeling
Zero-shot streaming text-to-speech is an important research topic in human-computer interaction. Existing methods primarily use a lookahead mechanism, relying on future text to achieve natural streaming speech synthesis, which introduces high processing latency. To address this issue, we propose SMLLE, a streaming framework for generating high-quality speech frame-by-frame. SMLLE employs a Transducer to convert text into semantic tokens in real time while simultaneously obtaining duration align…

This https://arxiv.org/abs/2405.08719 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However, recent work has demonstrated that model misspecification can compromise the reliability of SBI, preventing its adoption in important applications where only misspecified simulators are available. This work introduces robust posterior estimation~(RoPE), a framework that overcomes model misspecification with a small re…

Breaker: Removing Shortcut Cues with User Clustering for Single-slot Recommendation System
Chao Wang, Yue Zheng, Yujing Zhang, Yan Feng, Zhe Wang, Xiaowei Shi, An You, Yu Chen
https://arxiv.org/abs/2506.00828

Breaker: Removing Shortcut Cues with User Clustering for Single-slot Recommendation System
In a single-slot recommendation system, users are only exposed to one item at a time, and the system cannot collect user feedback on multiple items simultaneously. Therefore, only pointwise modeling solutions can be adopted, focusing solely on modeling the likelihood of clicks or conversions for items by users to learn user-item preferences, without the ability to capture the ranking information among different items directly. However, since user-side information is often much more abundant tha…

Modeling Cosmic Ray Electron Spectra and Synchrotron Emission in the Multiphase ISM
Nora B. Linzer, Lucia Armillotta, Eve C. Ostriker, Eliot Quataert
https://arxiv.org/abs/2507.00142

Modeling Cosmic Ray Electron Spectra and Synchrotron Emission in the Multiphase ISM
We model the transport and spectral evolution of 1-100 GeV cosmic ray (CR) electrons (CREs) in TIGRESS MHD simulations of the magnetized, multiphase interstellar medium. We post-process a kpc-sized galactic disk patch representative of the solar neighborhood using a two-moment method for CR transport that includes advection, streaming, and diffusion. The diffusion coefficient is set by balancing wave growth via the CR streaming instability against wave damping (nonlinear Landau and ion-neutral …

Agent-based modeling and the sociology of money: some suggestions for refining monetary theory using social simulation
Eduardo Coltre Ferraciolli, Tanya V. Ara\'ujo
https://arxiv.org/abs/2506.22318

Agent-based modeling and the sociology of money: some suggestions for refining monetary theory using social simulation
The institution of money can be seen as a foundational social mechanism, enabling communities to quantify collectively regulate economic processes. Money can be said, indeed, to constitute the micro-macro link in economics. This paper reviews influential views on the nature of money in economics and sociology, contrasting them to the relatively limited findings of recent agent-based models of "the emergence of money". Noting ample room for novel combinations of sociological and formal methods t…

Entropy of self-avoiding branching polymers: mean field theory and Monte Carlo simulations
Davide Marcato, Achille Giacometti, Amos Maritan, Angelo Rosa
https://arxiv.org/abs/2506.22315

Entropy of self-avoiding branching polymers: mean field theory and Monte Carlo simulations
We study the statistics of branching polymers with excluded-volume interactions, by modeling them as single self-avoiding trees on a generic regular periodic lattice with coordination number $q$. Each lattice site can be occupied at most by one tree node, and the fraction of occupied sites can vary from dilute to dense conditions. By adopting the statistics of directed trees as a proxy for that of undirected trees without internal loops and by an exact mapping of the model into a field theory, …

Shape Invariant 3D-Variational Autoencoder: Super Resolution in Turbulence flow
Anuraj Maurya
https://arxiv.org/abs/2507.22082 https://arxiv.org/pdf/2507.2…

Shape Invariant 3D-Variational Autoencoder: Super Resolution in Turbulence flow
Deep learning provides a versatile suite of methods for extracting structured information from complex datasets, enabling deeper understanding of underlying fluid dynamic phenomena. The field of turbulence modeling, in particular, benefits from the growing availability of high-dimensional data obtained through experiments, field observations, and large-scale simulations spanning multiple spatio-temporal scales. This report presents a concise overview of both classical and deep learningbased app…

Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks
Ehsan Roohi, Ahmad Shoja-sani
https://arxiv.org/abs/2506.22453 https://

Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks
This study presents a deep neural network (DNN) framework that accelerates Direct Simulation Monte Carlo (DSMC) computations for rarefied-gas flows, while maintaining high physical fidelity. First, a fully connected deep neural network is trained on high-quality DSMC data for seven temperatures (200-650 K) to reproduce the Maxwell-Boltzmann speed distribution of argon. Injecting the physical boundary point into the training set enforces the correct low-speed limit. It reduces the mean-squared e…