The Kosambi-Cartan-Chern (KCC) theory represents a powerful mathematical method for the investigation of the properties of dynamical systems. The KCC theory introduces a geometric description of the time evolution of a dynamical system, with the solution curves of the dynamical system described by methods inspired by t…
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48 results for “dynamic system theory”
Deep learning networks are approximated using dynamical systems theory.
problem Understanding the approximation capabilities of deep learning networks.
method Modeling deep residual networks as continuous-time dynamical systems and using approximation theories in .
result Established general sufficient conditions for universal approximation of deep residual networks.
A powerful mathematical method for the investigation of the properties of dynamical systems is represented by the Kosambi-Cartan-Chern (KCC) theory. In this approach the time evolution of a dynamical system is described in geometric terms, treating the solution curves of a dynamical system by geometrical methods inspir…
The present paper contains an interpretation and generalization of Novikov's theory of Morse type inequalities for 1-forms in terms of Conley's theory for dynamical systems.
Unified deep learning theory via dynamical systems and optimal control.
problem Lack of a unified framework in deep learning theory.
method Viewing deep neural networks as discrete-time nonlinear dynamical systems and optimization algorithms as controllers.
result Revealed convergence and generalization properties of training processes.
This paper studies the Yang--Mills ASD equation over the cylinder as a non-linear evolution equation. We consider a dynamical system consisting of bounded orbits of this evolution equation. This system contains many chaotic orbits, and moreover it becomes an infinite dimensional and infinite entropy system. We study th…
The new business paradigms originate a strong necessity to re-think the theory of the firm with the aim to get a better understanding on the organizational and functional principles of the firm, operating in the investment economies in the prosperous societies. In this connection, we make the innovative research to adv…
Formula derived for zeta functions of 3D foliated systems.
problem Analyzing zeta functions of 3D Riemannian foliated dynamical systems.
method Relating dynamical spectral -functions to zeta functions using the distributional dynamical Lefschetz trace formula.
result Proved a regularized determinant formula for zeta functions.
We discuss analogies between number theory and the theory of dynamical systems on spaces with a one-codimensional foliation. The emphasis is on comparing the "explicit formulas" of analytic number theory with certain dynamical Lefschetz trace formulas. We also point out a possible relation between an Arakelov-Euler cha…
The paper uses topological concepts to analyze neural networks, revealing complex structure and dynamics.
problem Understanding the structure and dynamics of deep learning models.
method Topological dynamical systems, index theory, and computational homology.
result Neurons correspond to simplexes in a simplicial complex, and topological invariants can be computed.
Class of Newtonian dynamical systems admitting normal blow-up of points in Riemannian manifolds is considered. Geometric interpretation for weak normality condition, which arose earlier in the theory of dynamical systems admitting the normal shift of hypersurfaces, is found.
The EM algorithm's convergence is analyzed using Lyapunov stability theory.
problem Analyzing the convergence of the EM algorithm.
method Reinterpreting the EM algorithm as a dynamical system and applying Lyapunov stability theory.
result Asymptotic stability and convergence of the EM algorithm are established.
Development of theory of dynamical systems admitting the normal shift in 1993-1999 is reviewed. Basics are given with complete proofs.
Survey on computational models in dynamical systems, including new universality concepts.
problem Understanding the relationship between computational models and dynamical systems.
method Review of recent works on Turing universality, Topological Kleene Field Theories, and dynamical bordisms.
result Introduction of new perspectives on computability through dynamical systems.
Problem of global integration of geometric structures arising in the theory of dynamical systems admitting the normal shift is considered. In the case when such integration is possible the problem of globalization for shift maps is studied.
Develops theory of contact systems with nonholonomic constraints.
problem Nonholonomic constraints in contact systems.
method Variational principle and projection of Hamiltonian vector field.
result Nonholonomic dynamics as projection of unconstrained dynamics.
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.
DVK model infers uncertainty-aware dynamical models for better control.
problem Uncertainty in nonlinear dynamical systems makes prediction and control challenging.
method Deep Variational Koopman (DVK) model infers distributions over observations.
result DVK model provides a distribution over dynamical models for long-term prediction and control.
Extends rigidity results to non-homogeneous manifolds.
problem Measure and topological rigidity in dynamical systems.
method From homogeneous to general manifolds.
result Measure and topological rigidity results extended.
In this paper we develope, in a geometric framework, a Hamilton-Jacobi Theory for general dynamical systems. Such a theory contains the classical theory for Hamiltonian systems on a cotangent bundle and recent developments in the framework of general symplectic, Poisson and almost-Poisson manifolds (including some appr…
Study on learning to predict dynamical systems without assuming their structure.
problem Learning to predict the next state of a dynamical system with unknown evolution function.
method Defined new combinatorial measures to quantify mistake and regret bounds in realizable and agnostic settings.
result In the realizable setting, the number of mistakes can grow arbitrarily with time.
The paper tackles learning optimal predictions from a single trajectory of a stochastic dynamical system.
problem Learning from a single finite trajectory of an ergodic stochastic dynamical system.
method The approach involves estimating the optimal one-step prediction function using nonlinear least squares and deriving high-probability guarantees.
result The study provides high-probability guarantees for the optimal prediction function, accounting for the non-independent and non-identically distributed nature of trajectory data.
New method identifies key genes affecting phenotypes in biological systems.
problem Identifying genes that drive specific phenotypes in complex biological systems.
method Data-driven observability decomposition using Koopman operators.
result Koopman operator representation identifies genes that drive phenotypes.
Two-dimensional case in the theory of dynamical systems admitting the normal shift differs crucially from multidimensional case. Features of two-dimensional case are gathered and studied in this thesis.
Geometric framework for dynamic feedback linearization of control systems with symmetry.
problem Dynamic feedback linearization of control systems with symmetry.
method Geometric framework based on Lie symmetry, systematic procedure for all smooth, generic system trajectories.
result Sufficient condition for dynamic feedback linearizability obtained.
This work develops a learning theory for inferring interaction kernels in complex agent systems.
problem Modeling complex interactions in systems of particles or agents.
method Nonparametric regression and approximation theory.
result Strong consistency and optimal convergence rates for estimators of interaction kernels.
A simple quantitative example of a reflexive feedback process and the resulting price dynamics after an exogenous price shock to a financial network is presented. Furthermore, an outline of a theory that connects financial reflexivity, which stems from cross-ownership and delayed or incomplete information, and no-arbit…
New algorithm learns linear dynamical systems from measurements.
problem Learning system dynamics from linear measurements efficiently and accurately.
method Method of moments estimator to directly estimate Markov parameters.
result First polynomial time algorithm for learning linear dynamical systems.
New tools for uncertainty in dynamical systems without distribution assumptions.
problem Uncertainty representation in dynamical systems without distributional assumptions.
method Kernel mean embedding and kernel probabilistic programming.
result Distribution-free representation, comparison, and propagation of uncertainties.
Neural network models colloidal particle dynamics in non-equilibrium systems.
problem Analyzing non-equilibrium dynamics of many-body colloidal systems.
method Combining power functional theory and machine learning, training a neural network to predict internal force fields.
result The neural network accurately predicts dynamics in non-equilibrium systems, in good agreement with simulations.
New method improves long-term forecasting of stochastic dynamical systems.
problem Improving long-term forecasting accuracy for stochastic dynamical systems.
method Combining Koopman and transfer operator theory with feature centering.
result Learning bounds ensure uniform performance on future distributions.
Koopman operator theory simplifies complex systems analysis.
problem Analyzing nonlinear dynamical systems and complex networks.
method Estimating Koopman operator from data to reveal system properties.
result Koopman operators provide insights into system characteristics.
Study variational submanifolds in Euclidean spaces from dynamical systems.
problem Formulate and solve conditions for variationality of induced systems on submanifolds.
method Employ variational sequence theory on sheaves of differential forms to analyze local and global variationality.
result Solve the problem of existence of variational submanifolds for second-order systems.
Survey of recent developments in symmetric reductions and controls for Hamiltonian systems.
problem Understanding the internal relationships of geometric structures and controls in Hamiltonian systems with symmetry.
method Survey and introduction of recent developments in controlled Hamiltonian systems with symmetry.
result Reveals the relationships between geometric structures, nonholonomic constraints, dynamical vector fields, and controls.
Formula for the force field of Newtonian dynamical systems admitting the normal shift of hypersurfaces in Riemannian manifolds is considered. Problem of globalization for geometric structures associated with this formula is studied.
On the Geometrical Gyro-Kinetic Theoryphysics.plasm-ph
Considering a Hamiltonian Dynamical System describing the motion of charged particle in a Tokamak or a Stellarator, we build a change of coordinates to reduce its dimension. This change of coordinates is in fact an intricate succession of mappings that are built using Hyperbolic Partial Differential Equations, Differen…
The paper explores the geometrical structures of phase spaces for controlled Hamiltonian systems with symmetry.
problem Understanding the dynamics and phase spaces of controlled Hamiltonian systems with symmetry.
method The paper uses Marsden-Weinstein reduction to define and analyze CH systems and their dynamics, focusing on the geometrical and topological structures of phase spaces.
result The paper reveals the relationships between the geometrical structures, dynamical vector fields, and controls of CH systems with symmetry.
Develops theory for data-driven methods in dynamical systems.
problem Lack of analysis for data-driven methods in dynamical systems.
method Establishes existence of mapping and properties of operator learning architecture.
result Novel universal approximation theorems for smoothing and forecasting.
Theory of Newtonian dynamical systems admitting normal shift of hypersurfaces was first developed for the case of Riemannian manifolds. Recently it was generalized for manifolds geometric equipment of which is given by some regular Lagrangian or, equivalently, by some regular Hamiltonian dynamical system. In present pa…
Combines Koopman theory and tensor trains for high-dimensional dynamical systems.
problem Analyzing complex, high-dimensional dynamical systems.
method Tensor train (TT) format for low-rank approximation, Koopman operator theory for dynamics.
result Efficient algorithms for low-rank representation of evolution operators.
We propose a projected gradient dynamical system as a model for a bargaining scheme for an asset for which the two interested agents have personal valuations which do not initially coincide. The personal valuations are formed using subjective beliefs concerning the future states of the world and the reservation prices …
Classifies 3D dynamical systems using foliations and topology.
problem Classifying partially hyperbolic systems in 3D.
method Use theory of foliations and topology.
result Introduces challenges and proposes steps for classification.
Quantum Econophysicsphysics.soc-ph
The relationships between game theory and quantum mechanics let us propose certain quantization relationships through which we could describe and understand not only quantum but also classical, evolutionary and the biological systems that were described before through the replicator dynamics. Quantum mechanics could be…
The paper investigates length averages in foliations, contrasting with time averages in dynamical systems.
problem Investigate the existence and non-existence of length averages in foliations.
method Generalize the existence problem of time averages in dynamical systems to foliations and introduce the concept of length averages.
result Length averages exist everywhere for codimension one orientable singular foliations without degenerate singularities on compact surfaces under a mild condition.
Examines predictability and complexity of economic time series using symbolic dynamics and entropy.
problem Understanding the predictability and complexity of economic time series.
method Symbolic dynamics and Information theory (entropy and uncertainty).
result Economic time series are complex and can be expressed in terms of information production.
SDCMs model causal dynamics of interacting components over time.
problem Modeling and understanding causal relationships in dynamical systems.
method Structural dynamical causal models (SDCMs) that represent time-dependent stochastic processes.
result SDCMs extend SEMs to include time-dependence and provide a theory for their solutions.
dynestyx: A library for probabilistic programming of dynamical systems
problem integrating state-space models into probabilistic programming languages
method a unified interface for specifying priors and performing inference
result principled uncertainty quantification for state and parameters
The abstract applies waist inequality to dynamical systems and entropy.
problem Understanding the relationship between waist inequality and dynamical systems.
method Applying waist inequality to entropy and mean dimension of dynamical systems.
result Maps between dynamical systems have positive conditional metric mean dimension under certain conditions.