Tootfinder

📦 upsert() keeps a single instance alive for singleton UI like toasts, loading overlays or progress bars — instead of stacking a new one, it updates the active instance with fresh data, no duplicates
⚙️ Mutation Flow: useMutationFlow from react-call/mutation-flow wires a callable straight to async backend actions, managing pending state and keeping the dialog open with a spinner until the promise resolves — so users can retry without losing their place, with end-to-end typed payloads…

A discrete Boltzmann model with state-dependent power-law relaxation time for nonequilibrium transport in compressible flows
Demei Li, Zhongyi He, Huilin Lai, Yanbiao Gan, Hailong Liu, Pengfei Lin
https://arxiv.org/abs/2605.18216 https://arxiv.org/pdf/2605.18216 https://arxiv.org/html/2605.18216
arXiv:2605.18216v1 Announce Type: new
Abstract: Thermodynamic nonequilibrium effects play a central role in momentum and energy transport in compressible flows. In conventional BGK kinetic models, the relaxation time $\tau$ is taken as a constant, which neglects the dependence of the relaxation process on local macroscopic states. To overcome this limitation, we develop a discrete Boltzmann model with a density- and temperature-dependent power-law relaxation time, termed DTRT-DBM, in which $\tau=\tau_0(\rho/\rho_0)^a(T/T_0)^b$. This formulation extends the discrete Boltzmann framework to flows with spatially varying nonequilibrium intensity. The model is validated by the Sod shock tube and by analytical solutions for viscous stress and heat flux, demonstrating accurate recovery of both macroscopic wave structures and nonequilibrium quantities across shock waves, rarefaction waves, and contact discontinuities. On this basis, phase diagrams of viscous stress and heat flux are constructed to examine how these quantities depend on the power-law exponents $a$ and $b$. The extrema of these quantities depend exponentially on the model parameters and exhibit regime-dependent behaviour. The roles of $a$ and $b$ are not symmetric: the nonequilibrium response is more sensitive to $a$ when density gradients dominate, but more sensitive to $b$ when temperature gradients dominate. Within the parameter range and flow configurations examined here, higher-order viscous stress increases the growth rate of the total viscous-stress extremum, whereas higher-order heat flux reduces the growth rate of the total heat-flux extremum. These results show that the proposed model can capture different higher-order nonequilibrium responses in compressible flows and provides a framework for the modelling and analysis of multiscale nonequilibrium processes.
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Replaced article(s) found for physics.flu-dyn. https://arxiv.org/list/physics.flu-dyn/new
[1/1]:
- Mixing Fronts in Smooth Chaotic Flows
Heyman Joris, Le Borgne Tanguy, Lester Daniel
https://arxiv.org/abs/2506.15255 https://mastoxiv.page/@arXiv_physicsfludyn_bot/114709421274451465
- Real-time reinforcement learning for turbulent state-dependent control in a bluff-body wake
Junjie Zhang, Chengwei Xia, Xianyang Jiang, Isabella Fumarola, Georgios Rigas
https://arxiv.org/abs/2509.11002 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115213145016358332
- Toward a unified data-driven turbulence model through multi-objective learning
Zhuoran Liu, Haochen Wang, Zhuolin Zhao, Heng Xiao
https://arxiv.org/abs/2509.17189 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115252943951151336
- Localization of sources in weakly nonlinear fluid systems using linear and quadratic sensitivity ...
Qi Wang, Zejian You
https://arxiv.org/abs/2601.06304 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115887064467176458
- Structures of elastoinertial turbulence in pipe flow
Manish Kumar, Michael D. Graham
https://arxiv.org/abs/2601.15637 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115943599169371363
- Revisiting the Frictional Control of the Antarctic Circumpolar Current From the Energy Diagram
Takuro Matsuta, Yuki Tanaka, Atsushi Kubokawa
https://arxiv.org/abs/2602.23742 https://mastoxiv.page/@arXiv_physicsfludyn_bot/116158781888135470
- Fluid dynamics as intersection problem
Nikita Nekrasov, Paul Wiegmann
https://arxiv.org/abs/2512.25053 https://mastoxiv.page/@arXiv_hepth_bot/115819554836846738
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SF-Flow: Sound field magnitude estimation via flow matching guided by sparse measurements
Ege Erdem, Shoichi Koyama, Tomohiko Nakamura, Orchisama Das, Zoran Cvetkovi\'c
https://arxiv.org/abs/2605.10398 https://arxiv.org/pdf/2605.10398 https://arxiv.org/html/2605.10398
arXiv:2605.10398v1 Announce Type: new
Abstract: Reconstructing a 3D sound field from sparse microphone measurements is a fundamental yet ill-posed problem, which we address through Acoustic Transfer Function (ATF) magnitude estimation. ATF magnitude encapsulates key perceptual and acoustic properties of a physical space with applications in room characterization and correction. Although recent generative paradigms such as Flow Matching (FM) have achieved state-of-the-art performance in speech and music generation, their potential in spatial audio remains underexplored. We propose a novel framework for 3D ATF magnitude reconstruction as a guided generation task, with a 3D U-Net conditioned by a permutation-invariant set encoder. This architecture enables reconstruction from an arbitrary number of sparse inputs while leveraging the stable and efficient training properties of FM. Experimental results demonstrate that SF-Flow achieves accurate reconstruction up to \SI{1}{kHz}, trains substantially faster than the autoencoder baseline, and improves significantly with dataset size.
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Kinetic-Optimal Scheduling with Moment Correction for Metric-Induced Discrete Flow Matching in Zero-Shot Text-to-Speech
Dong Yang, Yiyi Cai, Haoyu Zhang, Yuki Saito, Hiroshi Saruwatari
https://arxiv.org/abs/2605.09386 https://arxiv.org/pdf/2605.09386 https://arxiv.org/html/2605.09386
arXiv:2605.09386v1 Announce Type: new
Abstract: Metric-induced discrete flow matching (MI-DFM) exploits token-latent geometry for discrete generation, but its practical use is limited by two issues: heuristic schedulers requiring hyperparameter search, and finite-step path-tracking error from its first-order continuous-time Markov chain (CTMC) solver. We address both issues. First, we derive a kinetic-optimal scheduler for prescribed scalar-parameterized probability paths, and instantiate it for MI-DFM as a training-free numerical schedule that traverses the path at constant Fisher-Rao speed. Second, we introduce a finite-step moment correction that adjusts the jump probability while preserving the CTMC jump destination distribution. We validate the resulting method, GibbsTTS, on codec-based zero-shot text-to-speech (TTS). Under controlled comparisons with a unified architecture and large-scale dataset, GibbsTTS achieves the best objective naturalness and is preferred in subjective evaluations over masked discrete generative baselines. Additionally, in comparison with the evaluated state-of-the-art TTS systems, GibbsTTS shows strong speaker similarity, achieving the highest similarity on three of four test sets and ranking second on the fourth. Project page: https://ydqmkkx.github.io/GibbsTTSProject
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