3d-대형-언어-모델을-위한-통합-장면-표현-및-재구성

adapting-mixture-of-vision-experts-to-enhance-multimodal-understanding-in-large-language-models

マルチモーダル文脈に適応するビジョン専門家の混合体

다양한-비전-전문가들을-멀티모달-컨텍스트에-적응시키는-mova

estimating-response-functions-of-high-dimensional-dynamical-systems-using-score-based-generative-modeling

高次元力学系の応答理論における生成スコアモデリングの活用

반응-이론-생성-점수-모델링을-통한-접근

efficient-microcanonical-population-annealing-algorithm-for-analyzing-the-blume-capel-model-with-second-order-and-first-order-phase-transitions


Passive flow interactions lead to the spontaneous self-organization of groups of flapping swimmers into stable spatial formations that differentially distribute hydrodynamic benefits among the group members.


coremsg

The coordinated motion of animal groups through fluids is thought to reduce the cost of locomotion to individuals in the group. However, the connection between the spatial patterns observed in collectively moving animals and the energetic benefits at each position within the group remains unclear. To address this knowledge gap, we study the spontaneous emergence of cohesive formations in groups of fish, modeled as flapping foils, all heading in the same direction. We show in pairwise formations and with increasing group size that (1) in side-by-side arrangements, the reciprocal nature of flow coupling results in an equal distribution of energy requirements among all members, with reduction in cost of locomotion for swimmers flapping inphase but an increase in cost for swimmers flapping antiphase, and (2) in inline arrangements, flow coupling is non-reciprocal for all flapping phase, with energetic savings in favor of trailing swimmers, but only up to a finite number of swimmers, beyond which school cohesion and energetic benefits are lost at once. We explain these findings mechanistically and we provide efficient diagnostic tools for identifying locations in the wake of single (and multiple) swimmers that offer opportunities for hydrodynamic benefits to aspiring followers. Our results imply a connection between the resources generated by flow physics and social traits that influence greedy and cooperative group behavior.

### Competing Interest Statement

The authors have declared no competing interest.

Mapping Spatial Patterns to Energetic Benefits in Groups of Flow-coupled Swimmers

hydrodynamic-benefits-and-self-organization-in-groups-of-flow-coupled-swimmers


Hydrodynamic Benefits and Self-Organization in Groups of Flow-Coupled Swimmers


title_rewrite


For any fluid whose equation of state is a scaled monomial, such as the ideal gas, a unique generalized potential-solution exists for the steady-state fluid flow equations in pipeline networks. For non-ideal gases following the CNGA equation of state, while the existence of a generalized pressure-solution remains open, an alternative system is constructed that always has a unique solution, and this solution is a good approximation of the true solution.


on-the-existence-of-unique-steady-state-solutions-to-the-equations-governing-fluid-flow-in-networks


On the Existence of Unique Steady-State Solutions to the Equations Governing Fluid Flow in Networks



Applying electrical currents and magnetic fields can induce circulation and control the flow in Hele-Shaw cells, overcoming the inherent limitation of zero circulation in pressure-driven Hele-Shaw flows.


analytical-solutions-for-magnetohydrodynamic-flow-control-in-hele-shaw-cells


Analytical Solutions for Magnetohydrodynamic Flow Control in Hele-Shaw Cells



SineNet is a multi-stage neural network architecture that effectively models the temporal dynamics in time-dependent partial differential equations by reducing the misalignment in skip connections between the downsampling and upsampling paths.


sinenet-a-multi-stage-architecture-for-learning-temporal-dynamics-in-time-dependent-partial-differential-equations


SineNet: A Multi-Stage Architecture for Learning Temporal Dynamics in Time-Dependent Partial Differential Equations



Preconditioning with constant-density operators enhances convergence in variable-density incompressible flows.


variable-density-incompressible-flow-preconditioning-for-spectral-solution


Variable-Density Incompressible Flow Preconditioning for Spectral Solution



Machine learning models accurately predict energy budgets in droplet dynamics using geometric data.


predicting-energy-budgets-in-droplet-dynamics-a-machine-learning-approach


Predicting Energy Budgets in Droplet Dynamics: A Machine Learning Approach



Nonlinear convection is key to achieving global stabilization in 2D channel flow control.


2d-channel-flow-control-with-convection-for-global-stabilization


2D Channel Flow Control with Convection for Global Stabilization



Nonlinear convection is key to achieving global stabilization of parabolic Poiseuille profiles in 2D channel flows.


convection-enabled-boundary-control-of-a-2d-channel-flow-global-stabilization-of-parabolic-poiseuille-profiles


Convection-Enabled Boundary Control of a 2D Channel Flow: Global Stabilization of Parabolic Poiseuille Profiles



Establishing functional integral representations for solutions of motion equations in wall-bounded viscous flows using perturbation techniques.


random-vortex-dynamics-and-monte-carlo-simulations-for-wall-bounded-viscous-flows


Random Vortex Dynamics and Monte-Carlo Simulations for Wall-Bounded Viscous Flows



Developing a non-intrusive data-driven reduced order model using LSTM and POD techniques for CFD-DEM simulations.


developing-a-lstm-enhanced-surrogate-model-for-particle-laden-fluid-systems


Developing a LSTM-Enhanced Surrogate Model for Particle-Laden Fluid Systems
