arXiv research

Study periodic solutions in N-body problem, revealing braids with complex dynamics.

We provide new insight into the analysis of N-body problems by studying a compactification $M_N$ of $\mathbb{R}^{3N}$ that is compatible with the analytic properties of the $N$-body Hamiltonian $H_N$. We show that our compactification coincides with the compactification introduced by Vasy using blow-ups in order to stu…

Since the foundational work of Chenciner and Montgomery in 2000 there has been a great deal of interest in choreographic solutions of the n-body problem: periodic motions where the n bodies all follow one another at regular intervals along a closed path. The principal approach combines variational methods with symmetry…

The simplest non-collision solutions of the N-body problem are the "relative equilibria", in which each body follows a circular orbit around the centre of mass and the shape formed by the N bodies is constant. It is easy to see that the moment of inertia of such a solution is constant. In 1970, D. Saari conjectured tha…

COCA accelerates $N$-body simulations by correcting ML errors.

Marchal's lemma is the basic tool for eliminating collisions when using the direct method of the calculus of variations to establish existence of "designer" solutions to the classical N-body problem. Our goal here is to understand why Marchal's lemma holds, by taking a metric geometry perspective and employing the Jaco…

New periodic solutions found in 2n-body problem, braids of pseudo-Anosov type with stretch factors as metallic ratios.

Residual neural networks improve collision prediction in planetary simulations.

Bayesian Neural Networks improve precision cosmology from simulations.

Our recent study of a nation-wide production network uncovered a community structure, namely how firms are connected by supplier-customer links into tightly-knit groups with high density in intra-groups and with lower connectivity in inter-groups. Here we propose a method to visualize the community structure by a graph…

New approach reduces particle simulation complexity to linear time and space.

CHARM creates mock halo catalogs from dark matter density fields using neural networks.

In this paper we characterize planar central configurations in terms of a sectional curvature value of the Jacobi-Maupertuis metric. This characterization works for the $N$-body problem with general masses and any $1/r^α$ potential with $α> 0$. We also observe dynamical consequences of these curvature values for relati…

We prove existence and multiplicity of periodic motions for the forced 2-body problem under conditions of topological character. In the different cases, the lower bounds obtained for the number of solutions are related to the winding number of a curve in the plane, the homology of a space in $\R^3$, the knot type of a …

New emulator bridges simulators using conditional optimal transport.

Given a collection of N solutions of the (3+1) vacuum Einstein constraint equations which are asymptotically Euclidean, we show how to construct a new solution of the constraints which is itself asymptotically Euclidean, and which contains specified sub-regions of each of the N given solutions. This generalizes earlier…

New periodic solution found in 4-body problem, not part of expected geometrical family.

Dark matter in the universe evolves through gravity to form a complex network of halos, filaments, sheets and voids, that is known as the cosmic web. Computational models of the underlying physical processes, such as classical N-body simulations, are extremely resource intensive, as they track the action of gravity in …

Bayesian neural network predicts planetary instability.

Let $(M,g)$ be a globally symmetric space of noncompact type, of arbitrary rank, and $Δ$ its Laplacian. We prove the existence of a meromorphic continuation of the resolvent $(Δ-\ev)^{-1}$ across the continuous spectrum to a Riemann surface multiply covering the plane. The methods are purely analytic and are adapted fr…

The paper explores scaling symmetries in symplectic geometry and their applications to central configurations.

Running hydrodynamical simulations to produce mock data of large-scale structure and baryonic probes, such as the thermal Sunyaev-Zeldovich (tSZ) effect, at cosmological scales is computationally challenging. We propose to leverage the expressive power of deep generative models to find an effective description of the l…

The $N$-body problem with a $1/r^2$ potential has, in addition to translation and rotational symmetry, an effective scale symmetry which allows its zero energy flow to be reduced to a geodesic flow on complex projective $N-2$-space, minus a hyperplane arrangement. When $N=3$ we get a geodesic flow on the two-sphere min…

Study regularity of Schrödinger eigenfunctions with Coulomb-type potentials.

We perform an optimal localization of asymptotically flat initial data sets and construct data that have positive ADM mass but are exactly trivial outside a cone of arbitrarily small aperture. The gluing scheme that we develop allows to produce a new class of $N$-body solutions for the Einstein equation, which patently…

This paper presents a novel formulation and solution of orbit determination over finite time horizons as a learning problem. We present an approach to orbit determination under very broad conditions that are satisfied for n-body problems. These weak conditions allow us to perform orbit determination with noisy and high…

Constructs initial data for multiple black holes with specified ADM parameters.

Geometric Algebra Transformer (GATr) handles various geometric data types efficiently.

The paper presents an O(N log N)-implementation of t-SNE -- an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots and that normally runs in O(N^2). The new implementation uses vantage-point trees to compute sparse pairwise similarities between the input data object…

We introduce an approach for imposing physically motivated inductive biases on graph networks to learn interpretable representations and improved zero-shot generalization. Our experiments show that our graph network models, which implement this inductive bias, can learn message representations equivalent to the true fo…

A2I Transformer predicts atom energies from coordinates, avoiding heavy featurization.

IETNet identifies important channels for MVTS classification.

We introduce a Maximum Entropy model able to capture the statistics of melodies in music. The model can be used to generate new melodies that emulate the style of the musical corpus which was used to train it. Instead of using the $n-$body interactions of $(n-1)-$order Markov models, traditionally used in automatic mus…

Paper introduces a new sampling method combining Consistency Models with importance sampling.

This work discovers latent field effects governing interacting dynamical systems.

New method trains neural samplers to sample from multi-modal distributions efficiently.

Pairs (Hamiltonian system, Lagrangian distribution), called dynamical Lagrangian distributions, appear naturally in Differential Geometry, Calculus of Variations and Rational Mechanics. The basic differential invariants of a dynamical Lagrangian distribution w.r.t. the action of the group of symplectomorphisms of the a…

SE(3)-Transformers maintain equivariance for 3D data under rotations and translations.

iDEM generates samples from Boltzmann densities without data.

DSoftKI scales GP regression with full derivative observations.

A n n-body system is a labelled collection of n point masses in Euclidean space, and their congruence and internal symmetry properties involve a rich mathematical structure which is investigated in the framework of equivariant Riemannian geometry. Some basic concepts are n-configuration, configuration space, internal s…

Novel neural network layer improves long-range interactions in point clouds.

This work connects symmetries and conserved quantities in machine learning.

Hybrid model speeds up galaxy simulations by incorporating baryonic properties.

LAAT detects multiple low-density manifolds in noisy data.

Novel CG-EGNNs learn equivariant functions from Clifford algebras.

Study shows limitations and universality of equivariant QNNs with $S_n$-equivariant gates.

A framework to compare atomistic descriptors and their transformations.