Study self-interaction effects on point particle construction in GR.
problem Understanding mass in point particle initial data in GR.
method Introduce framework, rigorously prove vanishing mass, identify geometric structure and scaling parameter.
result Identify conditions for non-zero mass in point particle construction.
This work connects point particles to spin chains using geometric methods.
problem Understanding dynamics of free point particles on Riemannian manifolds.
method Kirillov orbit method, geometric quantization, Lagrangian submanifolds.
result Establishes a spectral equivalence between Laplace-Beltrami operator and a spin Hamiltonian.
Graph neural network constructs a sparse latent point cloud from dense point clouds.
problem Efficiently reconstructing and simulating point clouds with fine details.
method Irregular graph convolutional neural network with non-isotropic operations.
result The model can reconstruct dense point clouds from a sparse latent representation.
MPM-ParVI uses particle sampling for variational inference.
problem Variational inference for complex probabilistic models.
method Material Point Method (MPM) for particle-based simulation.
result Deterministic sampling and inference for intractable densities.
This work finds mixed equilibria in zero-sum games using interacting particle dynamics.
problem Finding mixed equilibrium points in continuous minmax games.
method A method based on entropic regularisation of two-layer zero-sum games with interacting particle dynamics.
result The sequence of empirical measures of the particle system satisfies a large deviation principle as the number of particles grows to infinity, implying convergence of the empirical measure and the Nikaidô-Isoda error.
A new model for generating point processes with complex geometries.
problem Difficulties in modeling point processes with large numbers of particles and complex geometries.
method Gradient descent algorithm applied to a phase harmonic operator on wavelet transforms of point patterns.
result The model allows for fast sampling of new configurations that match the statistics of observed point processes.
A new method solves high-dimensional MFGs using particle-based flow matching.
problem Solving high-dimensional Mean-Field Games (MFGs) is computationally challenging.
method Proposes a particle-based deep Flow Matching (FM) method to update particles and train a flow neural network.
result Proves convergence of the scheme to a stationary point sublinearly and linearly under convexity assumptions.
Estimates log-likelihood of interacting particle systems using virtual particles.
problem Inconsistent estimation of finite-particle log-likelihood in large particle systems.
method Stochastic gradient estimate using continuous trajectory and virtual particle systems.
result Convergence to stationary points of limiting mean-field system's log-likelihood.
Interacting particle methods are increasingly used to sample from complex and high-dimensional distributions. These stochastic particle integration techniques can be interpreted as an universal acceptance-rejection sequential particle sampler equipped with adaptive and interacting recycling mechanisms. Practically, the…
A method makes particle filters differentiable without altering their forward pass.
problem Compatibility issues between particle filters and automatic differentiation.
method Introduces a correction to particle weights using the stop-gradient operator.
result Automatic differentiation produces good estimators for gradients and second-order derivatives.
PSAEM combines EM and particle methods for efficient dynamical system learning.
problem Learning dynamical systems with stochastic approximation and particle methods.
method Particle stochastic approximation EM (PSAEM) algorithm combining stochastic approximation EM and particle Gibbs with ancestor sampling (PGAS).
result PSAEM achieves superior computational performance and convergence compared to existing methods.
We develop a gluing construction which adds scaled and truncated asymptotically Euclidean solutions of the Einstein constraint equations to compact solutions with potentially non-trivial cosmological constants. The result is a one-parameter family of initial data which has ordinary and scaled "point-particle" limits an…
Deep Sets improve jet discrimination in particle physics.
problem Representing and learning from collider events with variable-length particle sets.
method Energy Flow Networks and Particle Flow Networks, based on Deep Sets framework.
result Improved or similar performance in discriminating quark jets from gluon jets compared to existing methods.
New particle algorithms optimize latent variable models.
problem Optimizing latent variable models for maximum likelihood estimation.
method Identify gradient flows associated with free energy functional and discretize them to create particle-based algorithms.
result Novel particle algorithms scale to high-dimensional settings and perform well in experiments.
Researchers can reconstruct a Yang-Mills potential from scattering data and travel times.
problem Recovering a Yang-Mills potential from lens data on compact manifolds.
method Using convexity assumptions, reconstructing the potential from scattering data and travel times.
result Lens rigidity for a particle in a Yang-Mills field is demonstrated.
Improves music composition with user-defined constraints using continuous time models.
problem Combining sequence models with user-defined constraints in continuous time.
method Introduces a novel particle filter scheme for continuous time point processes.
result The particle filter scheme yields superior results in a human listening test.
We consider quasifuchsian manifolds with "particles", i.e., cone singularities of fixed angle less than π going from one connected component of the boundary at infinity to the other. Each connected component of the boundary at infinity is then endowed with a conformal structure marked by the endpoints of the particle…
New method for online learning in interacting particle systems.
problem Parameter estimation in stochastic interacting particle systems.
method Stochastic approximation of gradient of asymptotic log likelihood using continuous observations.
result Convergence to stationary points of asymptotic log-likelihood under suitable assumptions.
GER learns particle dynamics from unpaired snapshots using physics-informed GANs.
problem Learning particle dynamics from unpaired snapshots with physics constraints.
method Physics-informed generative model to fit particle ensemble distributions.
result Inferred dynamics of particle ensembles governed by SODEs up to 100 dimensions.
Generative algorithms learn high-dimensional data efficiently and generate new samples.
problem Learning from scarce high-dimensional data.
method Lipschitz-regularized gradient flows and particle-based algorithms.
result Correctly transports gene expression data points with high dimensionality.
EggNet reconstructs particle tracks from hits using evolving graph attention networks.
problem Particle track reconstruction is computationally expensive and combinatorial.
method EggNet uses a one-shot object condensation approach with evolving graph attention networks.
result EggNet outperforms methods requiring fixed input graphs on TrackML dataset.
Equivariant neural network simplifies particle physics models.
problem Complexity and interpretability in particle physics classification.
method Lorentz group equivariant neural network architecture.
result Simplified, interpretable models with fewer parameters.
High-precision machine learning reduces particle physics simulations by orders of magnitude.
problem Reducing computational burden in particle physics simulations.
method Developed optimal training strategies and tuned machine learning regressors, including Deep Neural Networks with skip connections and boosted decision trees.
result Significantly reduced computational time by factors of 10^3 to 10^6 over first-principles simulations.
Paper develops a particle filter for rapid model parameter adaptation and change detection.
problem Rapidly adapting to changes in model parameters and distinguishing between regime shifts and stochastic volatility.
method Incorporates genetic algorithm elements into a particle filter for accelerated adaptation and change detection.
result The filter adapts to regime shifts extremely rapidly and provides a clear heuristic for distinguishing between regime shifts and stochastic volatility.
New algorithm clusters particle tracks for better trajectory recognition in noisy data.
problem Challenging automatic reconstruction of particle tracks from Active Target Time Projection Chambers data.
method Non-parametric algorithm based on hierarchical clustering of point triplets.
result Algorithm identifies and isolates non-analytical particle tracks with high recall and precision.
Unified framework for photon and massive particle hypersurfaces in stationary spacetimes.
problem Understanding photon and massive particle hypersurfaces in stationary spacetimes.
method Unified framework using Killing-invariant timelike hypersurfaces and associated Finsler structures.
result Conditions for a hypersurface to be a photon or massive particle hypersurface are established.
New method learns particle system potentials from unlabeled data.
problem Learning potentials of interacting particle systems from unlabeled data with trajectory information missing.
method Introduces a self-test loss function based on stochastic evolution equation.
result Method outperforms baseline methods in robust estimation of large, high-dimensional systems.
The concepts of relative velocity and acceleration, deviation velocity and acceleration and relative momentum of point particles in spaces (manifolds), the tangent bundle of which is equipped with a transport along paths, are introduced. If the tangent bundle is endowed also with a metric, it gives rise also to the not…
This paper tackles hidden state inference for HMMs using particle filtering.
problem Inference for hidden states under HMMs is challenging due to unavailable true labels.
method Adaptive conformal inference framework using particle filtering.
result The framework produces prediction sets with specific aggregated coverage levels.
Machine learning identifies jet substructure from boosted Higgs decays.
problem Distinguishing jets from boosted heavy particles from QCD jets.
method Spectral analysis using neural networks on angular scale.
result ANN of angular spectrum input performs similarly to existing taggers.
Bayesian methods are appealing in their flexibility in modeling complex data and ability in capturing uncertainty in parameters. However, when Bayes' rule does not result in tractable closed-form, most approximate inference algorithms lack either scalability or rigorous guarantees. To tackle this challenge, we propose …
A new algorithm computes Wasserstein barycenters without entropic regularization.
problem Computing Wasserstein barycenters efficiently and accurately.
method Free-support algorithm based on particle flow and Riemannian geometry.
result The algorithm avoids entropic regularization and is computationally tractable.
FPGAs enable real-time neural network inference for particle physics.
problem Low-latency, low-power requirements for particle physics.
method Developed hls4ml for building machine learning models in FPGAs.
result Neural network inference fits within modern FPGA resources with 100 ns latency.
Improves forecasting accuracy and uncertainty characterization for spatio-temporal data.
problem Lack of uncertainty characterization in classical and deep learning models for spatio-temporal data.
method Bayesian inference using particle flow for approximating the posterior distribution of hidden states.
result Our approach provides better uncertainty characterization while maintaining comparable accuracy.
A new method for Bayesian posterior approximation using greedy particle optimization.
problem Difficulties in obtaining posterior distributions for complex models.
method MMD-FW, which minimizes MMD in a greedy way by the Frank-Wolfe algorithm.
result Shows a linear finite sample convergence bound for MMD-FW.
Study connects Riemann-Finsler geometry to Lorentz-violating scalar fields.
problem Exploring the connection between Riemann-Finsler geometries and Lorentz-violating scalar fields.
method Deriving quadratic actions and classical relativistic point-particle lagrangians in various spacetime dimensions.
result Support for open conjectures about Riemann-Finsler geometries in Lorentz-violating field theories.
We prove two related results. The first is an ``Earthquake Theorem'' for closed hyperbolic surfaces with cone singularities where the total angle is less than π: any two such metrics in are connected by a unique left earthquake. The second result is that the space of ``globally hyperbolic'' AdS manifolds with ``parti…
Study of particle systems with singular interaction through hitting times, revealing new phenomena and equilibrium strategies.
problem Understanding and predicting times of fragility in particle systems with strategic connections.
method General driving processes, inhomogeneous connection structures, strategic particle connections, max-plus algebra.
result Characterization of times of fragility and system regularization in equilibrium.
Stein Variational Gradient Descent optimizes particle sets to match distribution expectations.
problem Efficiently approximating complex distributions in machine learning.
method Evolve particle sets to match the expectations of a given distribution using Stein operators and kernels.
result Particles can be used to exactly estimate expectations of functions on distributions, providing insights into kernel choice.
Study particle dynamics in non-differentiable fractal spaces.
problem Understanding motion in non-smooth, probabilistic geometries.
method Use fiber bundle theory to characterize multivalued geodesic trajectories.
result Developed a hybrid theory combining surface and stochastic process theories.
We consider an open domain with a compact boundary in an Euclidean space and a Schroedinger operator with magnetic field on this domain. We give sufficient conditions on the rate of growth of the magnetic field near the boundary which guarantees essential self-adjointness of this operator. From the physical point of vi…
Study Lie and Noether symmetries for second-order differential systems.
problem Analyzing symmetries of second-order differential systems with multiple variables.
method Geometric approach to solve symmetry conditions, using collineations of metrics.
result General form of symmetry vector and Noetherian conservation laws determined.
New method for LVEBMs using saddle-point optimization and Langevin updates.
problem Expressive generative modeling of latent variables with hidden structure.
method Reformulate LVEBM training as a saddle problem, using Langevin updates and gradient flows.
result Proves existence and convergence of the algorithm under standard assumptions, with improved ELBO bounds.
We develop the Lorentzian geometry of a crooked halfspace in 2+1-dimensional Minkowski space. We calculate the affine, conformal and isometric automorphism groups of a crooked halfspace, and discuss its stratification into orbit types, giving an explicit slice for the action of the automorphism group. The set of parall…
New deep learning methods improve track reconstruction in particle physics.
problem Reconstructing particle tracks from detector hits in GEM detectors.
method Two-stage approach combining hits preprocessing and deep neural networks.
result Deep neural networks can accurately reconstruct tracks without preprocessing.
The study identifies conjugate and cut points in ideal fluid motion configurations.
problem Understanding stability and re-convergence of fluid configurations.
method Existence and non-existence of conjugate points in specific fluid configurations, using geometric and physical analysis.
result Existence of conjugate points in Kolmogorov flows and non-existence in Arnold steady states.
We consider the two body problem with central interaction on two point homogeneous spaces from point of view of the invariant differential operators theory. The representation of the two particle Hamiltonian in terms of the radial differential operator and invariant operators on the symmetry group is found. The connect…
Constructs an asymptotic metric for moduli space of centred hyperbolic monopoles.
problem Analyzing the moduli space of centred hyperbolic monopoles.
method Point particle approximation and geodesic motion analysis.
result Obtains a hyperbolic analogue of negative mass Taub-NUT metric.