arXiv research

Symbolic regression finds simple formulas for implied volatility.

OccamNet finds interpretable symbolic fits to data efficiently.

ISR creates analytical relationships from data via invertible maps.

Analytic Kähler potentials yield analytic Bergman kernels.

Complex $b$-$6j$ symbols relate to hyperbolic tetrahedron volumes and determinants.

New tools for analyzing Kähler manifolds, proving operator algebra and asymptotic kernel.

PySR method automates discovering equations from data in chaotic dynamics and epidemics.

Study on opers over complex manifolds of dimension one.

Extract symbolic models from deep learning with inductive biases.

Researchers compute Wodzicki residue for pseudo-differential operators on compact Lie groups.

Study $b$-$6j$ symbols linking anti-de Sitter tetrahedra to hyperbolic geometry.

The Atiyah-Singer index theorem is a topological formula for the index of an elliptic differential operator. The topological index depends on a cohomology class that is constructed from the principal symbol of the operator. On contact manifolds, the important Fredholm operators are not elliptic, but hypoelliptic. Their…

Symbolic regression improved by incorporating prior knowledge.

Study evaluates clustering methods for Google Trends data.

The notion of topological degree is studied for mappings from the boundary of a relatively compact strictly pseudo-convex domain in a Stein manifold into a manifold in terms of index theory of Toeplitz operators on the Hardy space. The index formalism of non-commutative geometry is used to derive analytic integral form…

Extends machine learning models for analytic boundary conditions in differential equations.

SDE automatically recovers interpretable discrete distributions.

Develops new approach to recover CR structures from their Levi foliations.

In this note we study the analytical index of pseudo-differential operators by using the notion of (infinite dimensional) operator-valued symbols (in the sense of Ruzhansky and Turunen). Our main tools will be the McKean-Singer index formula together with the operator-valued functional calculus developed here.

Neural network learns equations from data, improving interpretability and extrapolation.

For a compact, connected, oriented Riemannian $3$-manifold $(M, g)$ with smooth boundary $\partial M$, we explicitly give a local representation and a full symbol expression for the electromagnetic Dirichlet-to-Neumann map by factorizing Maxwell's equations and using an isometric transform. We prove that one can recons…

Reinforcement learning algorithms can solve dynamic decision-making and optimal control problems. With continuous-valued state and input variables, reinforcement learning algorithms must rely on function approximators to represent the value function and policy mappings. Commonly used numerical approximators, such as ne…

In this paper, the elastic Dirichlet-to-Neumann map $Ξ_g$ is studied for the stationary elasticity system in a compact Riemannian manifold $(Ω,g)$ with smooth boundary $\partial Ω$. By overcoming methodological difficulties, we explicitly get matrix-valued full symbol for the elastic Dirichlet-to-Neumann map $Ξ_g$. We …

The paper models musical motif transformations in Beethoven's works.

A new algorithm speeds up sparse regression for discovering equations from data.

Symbolic regression finds two projective invariants capturing most of the Ricci-flat metric variation.

Developing mathematical models of dynamic systems is central to many disciplines of engineering and science. Models facilitate simulations, analysis of the system's behavior, decision making and design of automatic control algorithms. Even inherently model-free control techniques such as reinforcement learning (RL) hav…

Paper proves index theorem for self-adjoint elliptic boundary problems.

Discovering quasipotential equations from data using machine learning.

Abstract: Determines thermoelastic coefficients from boundary data.

We investigate the relative information efficiency of financial markets by measuring the entropy of the time series of high frequency data. Our tool to measure efficiency is the Shannon entropy, applied to 2-symbol and 3-symbol discretisations of the data. Analysing 1-minute and 5-minute price time series of 55 Exchang…

VaSST uses soft symbolic trees for probabilistic symbolic regression.

Smooth fractal trees via analytic generators, preserving combinatorial and geometric properties.

We describe dimensionally constrained symbolic regression which has been developed for mass measurement in certain classes of events in high-energy physics (HEP). With symbolic regression, we can derive equations that are well known in HEP. However, in problems with large number of variables, we find that by constraini…

Paper closes neural-symbolic learning loop with grammar model and back-search algorithm.

Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents. Reinforcement learning relies on learning from interactions with real world, which often requires an unfeasibly large amount of experience. Symbolic planning relies on manually crafted symbolic knowledge, which may …

For an arbitrary Riemannian manifold $X$ and Hermitian vector bundles $E$ and $F$ over $X$ we define the notion of the normal symbol of a pseudodifferential operator $P$ from $E$ to $F$. The normal symbol of $P$ is a certain smooth function from the cotangent bundle $T^*X$ to the homomorphism bundle $Hom (E,F)$ and dep…

Based on the ideas of Optimal Control, we introduce the new basic characteristic of a bracket generating distribution, the Jacobi symbol. In contrast to the classical Tanaka symbol, the set of Jacobi symbols is discrete and classifiable. We give an explicit and unified algebraic procedure for the construction of the ca…

Analyzes how quadratic differential trajectories change with variation, proving a wall-crossing formula.

Symbolic knowledge in neural models can inadvertently make them more vulnerable to adversarial attacks.

We introduce mod 3 triple Milnor invariants and triple cubic residue symbols for certain primes of the Eisenstein number field $\mathbb{Q}(\sqrt{-3})$, following the analogies between knots and primes. Our triple symbol generalizes both the cubic residue symbol and Rédei's triple symbol, and describes the decomposition…

Researchers extend asymptotic analysis to Bergman projections with Gevrey weights.

A formula for Rademacher symbols in triangle groups is provided.

The symbolic dynamics technique is well-known for low-dimensional dynamical systems and chaotic maps, and lies at the roots of the thermodynamic formalism of dynamical systems. Here we show that this technique can also be successfully applied to time series generated by complex systems of much higher dimensionality. Ou…

The paper classifies symbols of differential operators on vector bundles.

NeSS combines neural and symbolic approaches for better compositional generalization.

Defines transverse symbols for foliated manifolds and proves their K-homology class.

A new method for spotting symbols in CAD images reduces annotation costs and improves accuracy.