Every discrete subset in a complex domain is in a complex curve.
problem Embedding discrete subsets in complex domains.
method Proving every closed discrete subset is in a complex curve with any topology.
result Closed discrete subsets are contained in complex curves with any topology.
Universal functions and metrics with constant curvature on domains.
problem Finding universal functions and metrics with constant curvature.
method Proving Runge-type theorems and universality results for locally univalent functions, refining Heins' result.
result Existence of universal conformal metrics with constant curvature on hyperbolic domains.
The paper constructs complete holomorphic immersions in complex spaces.
problem Finding complete holomorphic immersions in complex spaces.
method Constructing complete injective holomorphic immersions in C2 and generalizing to closed submanifolds. result Complete holomorphic immersions in C2 and related spaces are constructed. Paper proposes a new method to speed up diffusion models.
problem High computational cost of sampling from diffusion models.
method Stochastic Runge-Kutta method for acceleration.
result Provable acceleration with reduced score function evaluations.
A new method solves American put options with high accuracy and speed.
problem Solving American put options with high accuracy and speed.
method Adaptive fourth-order Runge-Kutta-Fehlberg method coupled with a fourth-order compact scheme.
result The method provides a more accurate solution and better performance in terms of computational speed.
The Runge-Kutta-Legendre scheme improves pricing American options and other derivatives.
problem Pricing American options and other derivatives with improved accuracy and stability.
method Runge-Kutta-Legendre finite difference scheme applied to Black-Scholes and Heston models.
result Improved convergence and stability compared to existing schemes.
Runge-Kutta methods are the classic family of solvers for ordinary differential equations (ODEs), and the basis for the state of the art. Like most numerical methods, they return point estimates. We construct a family of probabilistic numerical methods that instead return a Gauss-Markov process defining a probability d…
Improved meta-learning framework using Runge-Kutta methods.
problem Efficient adaptation and shared representation across tasks.
method Extending MAML framework with Runge-Kutta method for optimization control.
result Improved performance on various tasks through refined optimization control.
The paper defines flexible domains for minimal surfaces in Euclidean spaces and explores their properties.
problem Understanding the flexibility of domains in Euclidean spaces for minimal surfaces.
method Investigates the concept of flexibility in terms of minimal surfaces contained in domains.
result Defines flexible domains and shows how they can be approximated by minimal immersions.
The abstract discusses families of holomorphic maps to Oka manifolds with approximation theorems.
problem Approximating \(J_b\)-holomorphic maps to Oka manifolds.
method Constructing continuous or smooth families of \(J_b\)-holomorphic maps to Oka manifolds with approximation on compact Runge sets.
result Runge and Mergelyan approximation theorems and Weierstrass interpolation theorem for families of open Riemann surfaces.
New probabilistic scheme combines deep learning with Runge-Kutta methods for solving PDEs.
problem Solving high-dimensional semi-linear parabolic PDEs efficiently.
method Probabilistic scheme using deep learning and Runge-Kutta methods.
result Crank-Nicolson schemes are efficient in terms of precision, computational cost, and numerical implementation.
New methods improve efficiency of sampling algorithms for complex systems.
problem Efficiently sampling from complex, high-dimensional probability distributions.
method Randomized Runge-Kutta-Nyström methods tailored for Hamiltonian flows.
result Quantitative 5/2-order L2-accuracy in approximating Hamiltonian flows. Direct discretization of ODE achieves gradient-based acceleration.
problem Achieving acceleration in gradient-based optimization.
method Direct discretization of a related ODE using Runge-Kutta integrators.
result Convergence to optimal solution at rate O(N−2s+1s). Developed a symplectic integrator for complex manifolds.
problem Simulating Hamiltonian systems on specific manifolds.
method Partitioned Runge--Kutta methods for Hamiltonian systems on products of Hamiltonian manifolds, with derived symplecticity conditions.
result Derived algebraic conditions for symplecticity of methods.
Rex solves the inverse problem for ODE/SDE solvers, improving precision and stability.
problem Inversion of ODE/SDE solvers is inaccurate and impractical for precision applications.
method Rex uses Lawson methods to convert explicit Runge-Kutta schemes into algebraically reversible ones.
result Rex achieves near-machine-precision reconstruction and improves generative models.
New algorithm improves sampling efficiency for complex systems.
problem Sampling with Markov chain Monte Carlo methods.
method Discretizing smooth Itô diffusions with stochastic Runge-Kutta.
result Sampling iterates converge to target distribution faster.
Note on instabilities in super-time-stepping methods for Heston model.
problem Instabilities in super-time-stepping methods applied to Heston model.
method Exploration of explicit super-time-stepping schemes (RK-Chebyshev, RK-Legendre) for Heston model.
result Relevance of stability remarks beyond super-time-stepping schemes.
We examine spaces of connected tri-/univalent graphs subject to local relations which are motivated by the theory of Vassiliev invariants. It is shown that the behaviour of ladder-like subgraphs is strongly related to the parity of the number of rungs: there are similar relations for ladders of even and odd lengths, re…
Introduces a new stochastic optimization method for deep learning.
problem Minimizing loss functions in deep neural networks.
method Introduces a second-order stochastic Runge-Kutta method and an adaptive SGD-G2.
result The method yields consistent minimization of loss functions and automatically adjusts learning rates.
New method speeds up SDE inference by matching moments to FPK equation.
problem Efficiency of sampling schemes in high-dimensional SDEs.
method Direct approximation of Fokker-Planck-Kolmogorov equation by matching moments.
result Fast, scalable inference in high-dimensional latent spaces.
The study explores holomorphic Legendrian curves and superminimal surfaces in complex projective and sphere spaces.
problem Characterizing and embedding holomorphic Legendrian curves and superminimal surfaces.
method Runge approximation theorem, bijective correspondence via twistor projection, finite genus analysis.
result Every open Riemann surface embeds into CP3 as a complete holomorphic Legendrian curve. The study quantifies the information needed for causal queries at different levels of Pearl's hierarchy.
problem How much additional information is needed for interventional and counterfactual queries compared to observational queries?
method Formalized via query-class description length, using Kolmogorov complexity of answer oracles induced by SCMs.
result Binary acyclic SCMs show a quadratic gap between observational and interventional descriptions, and a logarithmic gap between interventional and counterfactual descriptions.
New boundary treatment improves accuracy for complex PDEs.
problem Order reduction in high-order IMEX schemes for multidimensional PDEs.
method Novel boundary treatment algorithms for Cartesian meshes, treating implicit-explicit stages similarly to interior points.
result Recovery of designed order of convergence by numerical verification.
New method solves complex financial option pricing with varying time steps.
problem Pricing American options with varying time steps and regime switching.
method Explicit Runge-Kutta-Fehlberg scheme with fourth-order compact finite difference in space and high order analytical approximation.
result The method provides better performance in terms of computational speed and accuracy.
Neural circuits integrate continuous dynamics efficiently.
problem Efficiently integrating continuous neural dynamics for simulation and learning.
method Compact neural circuits for Runge-Kutta and Adams-Bashforth-Moulton methods.
result Equivalence of neural and numerical integration for polynomial systems.
New deep learning architecture learns martingales efficiently.
problem Efficiently learning martingales in financial derivatives pricing.
method High-order weak approximation algorithms of Runge-Kutta type.
result Deep neural networks based on this architecture learn martingales effectively.
Study magnetic perturbations in Riemannian and Lorentzian Calderón problems.
problem Determining metrics from boundary measurements under magnetic perturbations.
method Runge approximation for Riemannian case, microlocal analysis for Lorentzian case.
result Metrics can be uniquely determined in both Riemannian and Lorentzian cases under specific perturbations.
Given a closed complex hypersurface Z⊂CN+1 (N∈N) and a compact subset K⊂Z, we prove the existence of a pseudoconvex Runge domain D in Z such that K⊂D and there is a complete proper holomorphic embedding from D into the unit ball of CN+1. For N=1,…
A higher-order Runge-Kutta optimizer performs poorly compared to Adam when evaluated fairly.
problem Evaluating the performance of adaptive Runge-Kutta optimizers under strict conditions.
method Built and evaluated a representative Adam variant using a Bogacki-Shampine 3(2) RK pair, FSAL reuse, and local-error step control.
result The adaptive nature of the RK optimizer is illusory; it behaves like a fixed-step optimizer with gradient averaging.
The paper develops theory for holomorphic null curves in SL2(C).
problem Developing theory for holomorphic null curves in SL2(C).
method Establish Runge, Mergelyan, Mittag-Leffler, and Carleman type theorems for holomorphic null immersions.
result Proves every open Riemann surface admits a proper holomorphic null embedding into SL2(C).
Higher-order ODE solvers improve deep learning performance.
problem Improving deep learning performance using higher-order ODE solvers.
method Evaluation and improvement of Runge-Kutta (RK) methods for deep learning.
result Higher-order RK solvers can improve deep learning performance by incorporating key ingredients of optimizers.
In 1996, Nadirashvili used Runge's theorem to produce a complete minimal disc inside a ball in R^3. In this paper we generalize the techniques used by Nadirashvili to obtain new examples of complete minimal surfaces inside a ball in R^3, with the conformal structure of an annulus.
New definition of patient-specific root causes of disease using counterfactuals.
problem Lack of rigorous mathematical formulation for automatic detection of root causes.
method Proposes a counterfactual definition matching clinical intuition and uses Shapley values for causal contribution scores.
result Adapts to disease prevalence, accounts for noisy labels, and admits fast computation.
Novel IMEX scheme solves financial PDEs with mixed derivatives.
problem Numerical approximations for financial PDEs with mixed derivatives.
method Second order finite volume IMEX Runge-Kutta scheme.
result Achieves true second order convergence with non-regular initial conditions.
We derive the explicit formula for the joint Laplace transform of the Wishart process and its time integral which extends the original approach of Bru. We compare our methodology with the alternative results given by the variation of constants method, the linearization of the Matrix Riccati ODE's and the Runge-Kutta al…
The paper models star dynamics using Ricci flow and Perelman entropy, revealing chaotic behavior.
problem Modeling chaotic positional dynamics of stars in celestial systems.
method Discrete dynamical systems, Ricci flow, Perelman entropy, Lyapunov exponents, bifurcation analysis.
result Entropy increases exponentially, indicating challenging long-term star position prediction.
Geometric methods integrate Lie systems for optimal control problems.
problem Integrating Lie systems for optimal control problems.
method Geometric numerical methods based on Magnus expansions and Runge-Kutta-Munthe-Kaas.
result Accurate numerical solutions for Lie systems in optimal control problems.
This paper proposes a new method to learn integration schemes for complex ODEs.
problem Learning efficient integration schemes for non-linear ODEs and their identification.
method A novel framework to learn integration schemes that minimize an integration-related cost function.
result The proposed learning-based approach provides integration schemes close to analytical solutions.
The paper studies dynamical systems with evolving geometric structure using numerical methods.
problem Qualitative behavior of ODEs with varying geometric structure.
method Fourth-order Runge-Kutta scheme for numerical analysis.
result Qualitative transitions in system dynamics as rotation parameter varies.
An adaptive time-stepping controller improves stability and accuracy of ResNets.
problem Improving stability and performance of ResNets using adaptive time stepping.
method Developed an adaptive time-stepping controller based on Runge-Kutta-Fehlberg method.
result Demonstrated improved stability and accuracy of ResNets without additional overhead.
New causal distances improve evaluation of causal discovery algorithms.
problem Evaluating causal discovery algorithms using graphical distances is limited.
method Defined causal distances based on causal distributions rather than graphical structure.
result Improved evaluation of causal discovery algorithms on synthetic and real-world datasets.
ContinuousNet generalizes ResNets to continuous dynamical systems.
problem ResNets fail to be meaningful dynamical integrators.
method Embedding continuous dynamical systems into higher-order numerical integration schemes (Runge Kutta).
result ContinuousNet exhibits invariance to discrete time step sizes and numerical integration schemes.
LCA measures neural network training changes, revealing parameter contributions.
problem Limited understanding of neural network training process.
method LCA decomposes training trajectory using Runge-Kutta integrator.
result 50% of parameters help during training, some layers hurt overall.
We study a moduli space of ASD connections over S3×R. We consider not only finite energy ASD connections but also infinite energy ones. So the moduli space is infinite dimensional in general. We study the (local) mean dimension of this infinite dimensional moduli space. We show the upper bound on the …
We consider the Kepler problem on surfaces of revolution that are homeomorphic to S2 and have constant Gaussian curvature. We show that the system is maximally superintegrable, finding constants of motion that generalize the Runge-Lentz vector. Then, using such first integrals, we determine the class of surfaces tha…
New insights link algebraic and geometric properties of connections.
problem Understanding numerical integration on manifolds.
method Relating invariant connections to Lie algebra actions.
result Generalized classical results for invariant connections on algebroids.
We prove a version of the classical Runge and Mergelyan uniform approximation theorems for non-orientable minimal surfaces in Euclidean 3-space R3. Then, we obtain some geometric applications. Among them, we emphasize the following ones: 1. A Gunning-Narasimhan type theorem for non-orientable conformal surfaces. 2. An …
New method calculates geodesic distances in Gaussian random field manifolds.
problem Quantifying similarity between random fields in different regimes.
method Numerical method using geodesic distances in Gaussian random field manifolds.
result Estimation of geodesic distances for various initial conditions.