A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New method efficiently simulates fluid flows across various conditions.
The study identifies unique fluid flow patterns.
Paper develops a new fluid flow model with energy exchange through boundaries.
The paper explores the geometric properties of fluid flows and their symmetries.
Paper extends port-Hamiltonian model to include internal energy for compressible and incompressible flow.
Researchers identify surfaces with special fluid flow fields.
Investigates fluid flow perturbations using geometric theory.
Identifies most probable flows for Kunita SDEs in fluid dynamics.
FLUID uses flows to unify filtering and smoothing for complex systems.
The study identifies conjugate and cut points in ideal fluid motion configurations.
The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the…
Study Godbillon-Vey invariants in non-Lorentzian spacetimes and fluid dynamics.
Deep learning predicts fluid flow in porous media, accelerating simulations by orders of magnitude.
Existence of a conjugate point in the incompressible Euler flow on a sphere and an ellipsoid is considered. Misiolek (1996) formulated a differential-geometric criterion (we call M-criterion) for the existence of a conjugate point in a fluid flow. In this paper, it is shown that no zonal flow (stationary Euler flow) sa…
The sectional curvature of the volume preserving diffeomorphism group of a Riemannian manifold can give information about the stability of inviscid, incompressible fluid flows on . We demonstrate that the submanifold of the volumorphism group of the solid flat torus generated by axisymmetric fluid flows with swi…
Paper finds new criteria for conjugate points in fluid flows.
Paper develops physics-informed, boundary-constrained Gaussian process for fluid flow field reconstruction.
LFlows model fluid densities and velocities using invertible maps that satisfy the continuity equation.
Stable long-term predictions for fluid flows using neural networks.
Generative models tackle incompressible fluid flows by enforcing divergence-free constraints.
We prove short-time existence for the Einstein-Euler-Entropy system for non-isentropic fluids with data in uniformly local Sobolev spaces. The cases of compact as well as non-compact Cauchy surfaces are covered. The method employed uses a Lagrangian description of the fluid flow which is based on techniques developed b…
Study axisymmetric ideal fluids on 3-manifolds, proving Fredholm properties.
SPH-ParVI uses fluid dynamics to sample unknown densities efficiently.
Study reveals how to determine area and curvature from fluid flow resonances.
We present a deep generative model, named Monge-Ampère flow, which builds on continuous-time gradient flow arising from the Monge-Ampère equation in optimal transport theory. The generative map from the latent space to the data space follows a dynamical system, where a learnable potential function guides a compressible…
We consider the regularity of an interface between two incompressible and inviscid fluids flows in the presence of surface tension. We obtain local in time estimates on the interface in and the velocity fields in . These estimates are obtained using geometric considerations which show th…
Computational Fluid Dynamics (CFD) is a hugely important subject with applications in almost every engineering field, however, fluid simulations are extremely computationally and memory demanding. Towards this end, we present Lat-Net, a method for compressing both the computation time and memory usage of Lattice Boltzm…
We present a frame-invariant method for detecting coherent structures from Lagrangian flow trajectories that can be sparse in number, as is the case in many fluid mechanics applications of practical interest. The method, based on principles used in graph coloring and spectral graph drawing algorithms, examines a measur…
We study the problem of inviscid slightly compressible fluids in a bounded domain. We find a unique solution to the initial-boundary value problem and show that it is near the analogous solution for an incompressible fluid provided the initial conditions for the two problems are close. In particular, the divergence of …
We infer both microscopic and macroscopic behaviors of a three-dimensional chaotic fluid flow using reservoir computing. In our procedure of the inference, we assume no prior knowledge of a physical process of a fluid flow except that its behavior is complex but deterministic. We present two ways of inference of the co…
The Skew Mean Curvature Flow(SMCF) is a Schrödinger-type geometric flow canonically defined on a co-dimension two submanifold, which generalizes the famous vortex filament equation in fluid dynamics. In this paper, we prove the local existence and uniqueness of general dimensional SMCF in Euclidean spaces.
Hybrid model combines neural networks and fluid dynamics for efficient, generalized simulations.
Generative Adversarial Networks have been shown to be powerful in generating content. To this end, they have been studied intensively in the last few years. Nonetheless, training these networks requires solving a saddle point problem that is difficult to solve and slowly converging. Motivated from techniques in the reg…
The control of complex systems is of critical importance in many branches of science, engineering, and industry. Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy (e.g., wind, tidal, and combustion), transportation (e.g., planes, trains, and automo…
We consider the Hele-Shaw flow that arises from injection of two-dimensional fluid into a point of a curved surface. The resulting fluid domains have and are more or less determined implicitly by a mean value property for harmonic functions. We improve on the results of Hedenmalm and Shimorin \cite{HS} and obtain essen…
Researchers use quantum chaos and RMT to analyze turbulence, revealing unique scaling laws.
Generative models improve carbon storage site prediction using Bayesian inversion.
Paper predicts turbulent flows using physics-informed deep learning.
A new method for computing shape gradients in FSI problems with non-matching meshes.
New algorithms improve vascular flow simulations in aortic aneurysms.
New approach links 2D fluid dynamics to matrix theory.
Tichler proved that a manifold admitting a smooth closed one-form fibers over a circle. More generally a manifold admitting independent closed one-forms fibers over a torus . In this article we explain a version of this construction for manifolds with boundary using the techniques of -calculus. We explore n…
We present a novel technique for assessing the dynamics of multiphase fluid flow in the oil reservoir. We demonstrate an efficient workflow for handling the 3D reservoir simulation data in a way which is orders of magnitude faster than the conventional routine. The workflow (we call it "Metamodel") is based on a projec…
We consider the kinematics of specific fluid spacetimes admitting timelike congruences of Ricci Solitons. These fluids includes string cloud, string fluid, perfect fluid, radially symmetric fluid, anisotropic fluid and relativistic magneto-fluid. Results are obtained and important physical aspects are discussed.
The binormal (or vortex filament) equation provides the localized induction approximation of the 3D incompressible Euler equation. We present explicit solutions of the binormal equation in higher-dimensions that collapse in finite time. The local nature of this phenomenon suggests the appearance of singularity in nearb…
Physics-informed model reduces RBC simulation costs.
An accurate and fast estimation of the available bandwidth in a network with varying cross-traffic is a challenging task. The accepted probing tools, based on the fluid-flow model of a bottleneck link with first-in, first-out multiplexing, estimate the available bandwidth by measuring packet dispersions. The estimation…
Brain tumor segmentation from Magnetic Resonance Images (MRIs) is an important task to measure tumor responses to treatments. However, automatic segmentation is very challenging. This paper presents an automatic brain tumor segmentation method based on a Normalized Gaussian Bayesian classification and a new 3D Fluid Ve…