Paper proves estimates for Lagrangian flow singularities.
problem Understanding Lagrangian flow singularities.
method Interior a priori estimates and Jacobi inequality.
result Proves estimates for supercritical Lagrangian phase.
Support Vector Machines predict gas-liquid flow patterns with 97% accuracy.
problem Predicting gas-liquid flow patterns in multiphase flow systems.
method Support Vector Machine (SVM) applied to a dataset of two-phase flow patterns.
result Achieved 97% correct classification of flow patterns.
New tan-concavity property for Lagrangian phase operators helps in studying dHYM metrics.
problem Lack of concavity in Lagrangian phase operator for dHYM metrics.
method Introduce tangent Lagrangian phase flow (TLPF) on almost calibrated (1,1)-forms.
result TLPF exists for all positive time and converges to dHYM metrics under certain conditions.
Ancient symplectic solutions to mean curvature flow are flat.
problem Understanding ancient solutions to mean curvature flow in symplectic geometry.
method Using a complex phase map to prove a Bernstein theorem.
result Ancient solutions to the symplectic mean curvature flow are flat.
Study on critical Lagrangian phase singularities in mean curvature flow.
problem Analyzing singularities in the Lagrangian mean curvature flow at the critical phase.
method Developed new method to prove C2,α estimates by using concave operators. result Established interior estimates for critical Lagrangian phase singularities.
A new deep learning model improves phase retrieval performance.
problem Recovering signals from phaseless measurements.
method Hybrid model-based data-driven deep architecture (Unfolded Phase Retrieval, UPR).
result Significant improvement in phase retrieval performance.
Study proves existence of a specific type of flow in geometry.
problem Existence of canonical multi-phase free boundary Brakke flows.
method Global-in-time existence established using Brakke flow and uniform density ratio assumption.
result Existence of the flow with no positive mass on the free boundary for some short time.
Proves existence of multi-phase flows from arbitrary initial data.
problem Non-uniqueness issue in Brakke flows.
method Global existence proof for multi-phase mean curvature flow.
result Validates explicit identity for evolving grain volumes.
In this paper we study the phenomenon of phase transitions for the geodesic flow on some geometrically finite negatively curved manifolds. We define a class of potentials going slowly to zero through the cusps of M for which the pressure map exhibits a phase transition. By a careful choice of the metric at the cusp w…
A time schedule simplifies learning in flow-based models for high-dimensional data.
problem Disappearance of relative probability phase in high-dimensional Gaussian mixture sampling.
method Introduces a time dilation schedule to characterize phases of learning.
result Autoencoder learns to simplify by focusing on relevant parameters for each phase.
Generalizes Nambu mechanics using vector Hamiltonians.
problem Representing divergence-free phase flows in Rn. method Introduces generalized Nambu mechanics with n−1 integral invariants. result Any divergence-free phase flow can be represented as generalized Nambu mechanics.
Study shows thresholding scheme converges for mean curvature flow of convex sets.
problem Analyzing convergence of thresholding scheme for mean curvature flow.
method Time discretization using Merriman, Bence and Osher's scheme, focusing on two-phase mean convex settings.
result Time-integrated energy of approximation converges to limit's energy in minimizing movements interpretation.
Study finds weak solutions for complex map flows with optimal lifespan.
problem Existence of weak solutions for two-phase matrix-valued harmonic map flows.
method Modified minimizing movement scheme, discretizing time and interpolating solutions.
result Existence of weak solutions with optimal lifespan for the limiting system.
We study the phase field method for the volume preserving mean curvature flow. Given an initial C1 hypersurface we proved the existence of the weak solution for the volume preserving mean curvature flow via the reaction diffusion equation with a nonlocal term. We also show the monotonicity formula and the density up…
Gradient flow in phase retrieval escapes spurious minima with high probability.
problem Understanding gradient-based optimization in high-dimensional non-convex functions.
method Analytical and numerical study of gradient dynamics in phase retrieval.
result Gradient flow avoids spurious minima by drifting along unstable directions.
The paper studies mean curvature flow on hyperkähler manifolds and proves no Type 1 singularities.
problem Analyzing mean curvature flow on hyperkähler manifolds.
method Using optimal rigidity theorems and complex phase maps.
result No Type 1 singularities occur in mean curvature flow on hyperkähler manifolds.
Study gradient flow of phase transitions with fixed contact angle.
problem Understanding phase transitions with fixed contact angle.
method Gradient flow of the Allen-Cahn equation with fixed boundary contact angle.
result Established interior and boundary convergence properties for solutions and energy measures.
New model estimates Gibbs free energies using machine learning and isobaric-isothermal flows.
problem Estimating Gibbs free energies for complex systems.
method Normalizing flows trained to sample isobaric-isothermal ensemble.
result Excellent agreement with established baselines for water phases.
We consider the robust phase retrieval problem of recovering the unknown signal from the magnitude-only measurements, where the measurements can be contaminated by both sparse arbitrary corruption and bounded random noise. We propose a new nonconvex algorithm for robust phase retrieval, namely Robust Wirtinger Flow to …
New varifold solutions for mean curvature flow converge and are unique.
problem Mean curvature flow and Allen-Cahn equation convergence and uniqueness.
method Evolving varifolds coupled to phase volumes, weak-strong uniqueness principle.
result Limits of Allen-Cahn solutions are varifold solutions, and classical flows are unique.
On the basis of Liouville theorem the generalization of the Nambu mechanics is considered. For three-dimensional phase space the concept of vector hamiltonian and vector lagrangian is entered.
NeuralFLoC unifies registration and clustering of functional data, overcoming phase variation challenges.
problem Challenges in clustering functional data due to phase variation and temporal misalignment.
method NeuralFLoC uses Neural ODE-driven diffeomorphic flows and spectral clustering for joint registration and clustering.
result NeuralFLoC effectively disentangles phase and amplitude variation, achieving state-of-the-art performance.
Paper uses smart meter data to accurately estimate multi-phase topology and identify bus phases in unbalanced distribution grids.
problem Accurate topology knowledge is needed for monitoring and controlling uncertainties in unbalanced distribution grids.
method Converts multi-phase unbalanced systems into symmetrical components and uses information theory, power flow equations, and conditional independence relationships to estimate topology and identify bus phases.
result The algorithm accurately estimates multi-phase topology and identifies bus phases in unbalanced distribution grids, even with strong load unbalancing and DERs.
New approach to analyze matrix denoising using gradient flow and fixed point equations.
problem Positive semi-definite matrix denoising in extensive-rank and high-dimensional settings.
method Gradient flow and fixed point equations derived from linear pencil techniques of random matrix theory.
result Continuous phase transitions in the extensive-rank and high-dimensional regime.
Gradient flow with weight decay shows grokking effect in deep learning.
problem Understanding the grokking effect in deep learning.
method Analyzing gradient flow dynamics with weight decay.
result Weight decay causes slow norm reduction, explaining grokking.
Study on multi-head softmax attention dynamics for in-context learning.
problem Understanding and optimizing multi-head softmax attention models for multi-task linear regression.
method Gradient flow analysis and spectral mapping technique.
result Gradient flow converges to optimal multi-head softmax attention model, with task allocation emerging during training.
Study sharp interface limit of Allen-Cahn equation to characterize mean curvature flow with boundary conditions.
problem Characterizing mean curvature flow with Dirichlet or dynamic boundary conditions.
method Varifold formulation, phase field method, extending Brakke flow.
result Sharp interface limit of Allen-Cahn equation converges to mean curvature flow with boundary conditions.
New phase harmonic covariance models capture non-Gaussian properties of stationary processes.
problem Capturing non-Gaussian properties of stationary processes using Fourier phase.
method Introduce phase harmonic covariance moments and maximum entropy models conditioned by these moments.
result Maximum entropy models from phase harmonic covariances improve image synthesis of turbulent flows.
Study on line bundle flow on Kähler surfaces converging to a singular solution.
problem Analyzing the mean curvature flow on Kähler surfaces.
method Investigates the flow under hypercritical phase and semipositivity conditions.
result The flow converges to a singular solution away from curves of negative self-intersection.
Physics-guided deep learning improves CFD for bubbly flow simulations.
problem Accurate CFD prediction of two-phase bubbly flow with high computational efficiency.
method Developed a multi-scale framework with Feature Similarity Measurement (FSM) for error estimation and a physics-guided deep feedforward neural network (DFNN) surrogate model.
result Physics-guided deep learning achieves comparable accuracy to fine-mesh simulations with fast-running feature.
Generative model learns spin-glass dynamics and properties.
problem Complex behavior of many-body systems in statistical physics and computer science.
method Self-supervised learning with normalizing flows.
result Key physical and computational properties of spin-glasses are learned.
Improves bounds on eigenfunctions using microlocal averages in phase space.
problem Improving Lp bounds on eigenfunctions in high frequency limit. method Develops sufficient conditions for microlocal averages in nonpositive curvature and partially hyperbolic flows.
result Improves microlocal averages for eigenfunctions in more general settings.
We present a generalization of the Nambu mechanics on the base of Liouville's theorem. We prove that the Poisson structure of an n-dimensional multisymplectic phase space is induced by (n-1)-Hamiltonian k-vector field seach of which requires introduction of k-Hamiltonians.
Paper tackles phase retrieval with robust gradient descent for noisy data.
problem Recover signals from magnitude measurements with noise and corruption.
method Robust gradient descent applied to Wirtinger Flow algorithm.
result Improves algorithm's robustness to heavy-tailed noise and adversarial corruption.
We explore a computational model of an incompressible fluid with a multi-phase field in three-dimensional Euclidean space. By investigating an incompressible fluid with a two-phase field geometrically, we reformulate the expression of the surface tension for the two-phase field found by Lafaurie, Nardone, Scardovelli, …
Study proves a new flow method for mean curvature with volume change analysis.
problem Existence of BV flow via mean curvature flow.
method Elliptic regularization to prove existence of generalized BV flow.
result Proves existence of generalized BV flow with volume change expression.
New algorithms handle phase retrieval with rank d measurements, revealing phase transitions.
problem Phase retrieval with rank d measurements.
method Random duality theory (RDT) and descending phase retrieval algorithms (dPR).
result Minimal sample complexity ratio for dPR's success exhibits phase transitions.
This paper considers the noisy sparse phase retrieval problem: recovering a sparse signal x∈Rp from noisy quadratic measurements yj=(aj′x)2+εj, j=1,…,m, with independent sub-exponential noise εj. The goals are to understand the effect of the sparsity of x on the estimation prec…
The paper proves Hessian estimates for specific geometric flows.
problem Proving interior Hessian estimates for specific geometric flows.
method Proved interior Hessian estimates for shrinkers, expanders, translators, and rotators of the Lagrangian mean curvature flow.
result Extended results to a broader class of Lagrangian mean curvature type equations.
Study shows how flat flow solutions in 2D converge to disks.
problem Understanding the asymptotics of area-preserving mean curvature flow in 2D.
method Analyzes flat flow solutions starting from bounded sets of finite perimeter.
result Flat flow solutions converge to a union of equally sized disks with exponential rate.
Novel weak solutions for volume-preserving mean curvature flow established.
problem Existence and uniqueness of solutions to volume-preserving mean curvature flow.
method Introducing varifold solutions coupled with phase volumes and new calibrations.
result Uniqueness of classical solutions among varifold solutions.
We give a global picture of the Ricci flow on the space of three-dimensional, unimodular, nonabelian metric Lie algebras considered up to isometry and scaling. The Ricci flow is viewed as a two-dimensional dynamical system for the evolution of structure constants of the metric Lie algebra with respect to an evolving or…
New algorithm converges to optimal phase retrieval estimator with misspecified link functions.
problem High-dimensional sparse phase retrieval with incorrect model specification.
method Simple variant of thresholded Wirtinger flow algorithm, linear convergence for optimal accuracy.
result Linear convergence to optimal estimator for a broad family of unknown link functions.
In this paper we study the global behavior of the Ricci flow equation for two classes of homogeneous manifolds with two isotropy summands. Using methods of the qualitative theory of differential equations, we present the global phase portrait of such systems and derive some geometrical consequences on the structure of …
A new traffic signal control method using phase competition.
problem Improving urban transportation efficiency through advanced learning techniques.
method Intuitive phase competition principle applied to reinforcement learning for traffic signal control.
result Our model achieves better solutions, faster convergence, and superior generalizability compared to existing RL methods.
Model shows phase transitions in asset pricing with market maker incentives.
problem Analyzing asset pricing with market maker profit incentives.
method Stochastic game theory, neural networks.
result Equilibrium experiences three phases: linear pricing, mid-price with spread, and metastable state.
In work the internal structure of de Rham cohomology is considered. As examples the phase flows in R3 admitting the Nambu Poisson structure are studied.
Study flat flow solutions to Mullins-Sekerka and area-preserving curvature flows on planar flat torus.
problem Behavior of flat flow solutions on planar flat torus.
method Sharp quantitative Alexandrov inequality derivation for periodic smooth sets.
result Flat flows converge to specific configurations exponentially fast.