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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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48 results for complex systems

The paper sets sample complexity bounds for identifying LTI systems from a finite set.

problem Identifying an LTI system from a finite set of possible systems using trajectory data.
method Maximum likelihood estimator and information theory tools.
result Upper and lower bounds for sample complexity are derived, independent of stability assumption.

We introduce two numerical conjugacy invariants for dynamical systems -- the complexity and weak complexity indices -- which are well-suited for the study of "completely integrable" Hamiltonian systems. These invariants can be seen as "slow entropies", they describe the polynomial growth rate of the number of balls (fo…

2009-07-30abs ↗pdf ↗

Develops neural networks for learning physics of complex systems by enforcing thermodynamics principles.

problem Learning physics of complex systems from incomplete experimental data.
method Integrates port-metriplectic formalism with neural networks to enforce thermodynamics principles.
result Neural networks can learn physics of complex systems by parts, reducing learning burden.

Paper accelerates nonlinear mapping in online systems with lower time complexity.

problem Speeding up nonlinear mapping in online systems.
method Integrates an acceleration module into Dendrite Net (DD) to reduce time complexity.
result DD with AC has lower time complexity while maintaining nonlinear mapping and system identification properties.

Learning to control linear systems is statistically hard, especially for underactuated systems.

problem Statistical difficulty of learning to control linear systems, especially underactuated ones.
method Utilized minimax lower bounds and structural assumptions to prove learning complexity can be exponential.
result Learning complexity can be at most exponential with the controllability index of the system.

The paper explores growth rates and Perron numbers in Coxeter systems with low-dimensional Davis complexes.

problem Investigating growth rates and specific types of numbers in Coxeter systems with Davis complexes of low dimension.
method Examining Coxeter systems with Davis complexes of dimension at most 2, focusing on growth rates and specific types of numbers.
result The growth rate of Coxeter systems with Davis complexes of dimension at most 2 are either Salem or Pisot numbers, depending on the Euler characteristic.

New method integrates sparse parametric and nonparametric techniques for complex system modeling.

problem Lack of accurate modeling for complex biological systems due to nonlinearities.
method Sparse nonparametric estimation framework combining parametric and nonparametric techniques.
result Accurately captures nonlinearities in complex systems without prior information.

The paper explores when linear system identification is hard or easy, especially for under-actuated systems.

problem Statistical hardness of learning linear systems, especially under-actuated or under-excited systems.
method Using tools from minimax theory and recent statistical tools for finite sample analysis of system identification.
result The controllability index of linear systems affects the sample complexity of identification, making some systems hard to learn.

Optimal noise excitation for linear system identification reduces sample complexity.

problem Efficiently identifying linear systems with minimal data.
method Active learning algorithm using ordinary least squares and semidefinite programming.
result The proposed algorithm matches lower bounds on sample complexity for any active learning method.

The abstract explores a new wave equation linking quantum mechanics and complex adaptive systems.

problem Understanding the underlying mechanism of distribution formation in complex quantum entanglement.
method Exploring the logical relationship between Schrödinger's wave equation and Shi's trading volume-price wave equation in finance.
result A non-localized wave equation in quantum mechanics reveals the invariance of interaction as a universal law.

Generative models speed up complex system simulations.

problem Accurately forecasting the dynamics of complex systems at reduced cost.
method Generative Learning of Effective Dynamics (G-LED) using auto-regressive attention and Bayesian diffusion models.
result Generative models can accurately forecast complex system dynamics at lower computational cost.

Lower bounds and upper bounds on sample complexity for identifying linear dynamical systems.

problem Identifying an unknown linear dynamical system with limited data.
method Sample complexity lower and upper bounds, persistent excitation condition, active learning algorithm.
result Lower and upper bounds share the same dependency on key problem parameters.

Complex network theory has been applied to solving practical problems from different domains. In this paper, we present a general framework for complex network applications. The keys of a successful application are a thorough understanding of the real system and a correct mapping of complex network theory to practical …

2015-07-21abs ↗pdf ↗

This paper improves system identification by reducing sample complexity for high-dimensional linear dynamical systems.

problem High sample complexity for learning partially observed linear dynamical systems in high dimensions.
method Introduces an 1\ell_1-regularized estimation method that reduces sample complexity from linear to logarithmic with system dimension.
result Markov parameters can be learned with logarithmic number of samples relative to system dimension, improving sample complexity.

Paper reduces sample complexity for bilinear systems identification to nearly constant.

problem Identifying discrete-time bilinear systems under bounded disturbances.
method Uses trajectory-dependent regressors and polynomial mean-square state growth analysis.
result Proves sample complexity of O~(1/ε)\widetilde{\mathcal O}(1/ε) for estimation error εε.

Link Floer homology is split into snake complexes and local systems.

problem Classifying link Floer complexes over specific rings.
method Classifying isomorphism and chain homotopy equivalence classes of free chain complexes over a specific ring, then applying these results to link Floer complexes.
result Link Floer complexes split uniquely into snake complexes and local systems.

We describe and extract time-ordered multibody interactions from complex systems.

problem Complex systems with temporal and multibody dependencies.
method Decompose multivariate Markov chains into time-ordered multibody interactions. Algorithm to extract interactions from data. Measure complexity of interaction ensembles.
result Robust and efficient algorithm to infer time-ordered multibody interactions from data.

Neural networks improve predictions of complex network dynamics.

problem Improving neural network predictions for complex network dynamics.
method Extended neural network models to complex systems, ensuring they conform to dynamical model assumptions and using a statistical significance test.
result Achieved advanced generalization of neural network predictions for complex systems.

Modeling cascading behavior in complex systems using CTBNs.

problem Understanding which states trigger cascading events in complex systems.
method Continuous-time Bayesian networks (CTBNs) for modeling and identifying likely sentry states.
result Identification of likely sentry states that may lead to cascading behavior.

The paper argues that attracting more economists and adopting a more-precise definition of dynamic complexity might help econophysics acquire more attention in the economics community and bring new lymph to economic research. It may be necessary to concentrate less on the applications than on the basics of economic com…

2019-11-08abs ↗pdf ↗

Identifies bilinear systems from a single trajectory with optimal sample complexity.

problem Learning bilinear systems from a single trajectory of states and inputs.
method Uses a mild marginal mean-square stability assumption and martingale small-ball condition.
result Sample complexity and statistical error rates are optimal.

The paper develops algorithms and topological invariants for distinguishing dynamic systems.

problem Distinguishing the topological type of surfaces and functions in dynamic systems.
method Construction of algorithms and topological invariants using discrete topological structures.
result The development of discrete topological structures for topological equivalence of dynamic systems.

In the spirit of topological entropy we introduce new complexity functions for general dynamical systems (namely groups and semigroups acting on closed manifolds) but with an emphasis on the dynamics induced on simplicial complexes. For expansive systems remarkable properties are observed. Known examples are revisited …

2008-06-27abs ↗pdf ↗

Study shows neural operators can efficiently solve complex reaction-diffusion systems.

problem Efficiently solving nonlinear reaction-diffusion systems using neural operators.
method Laplacian-based neural operators applied to a generalized Gierer-Meinhardt system.
result Explicit approximation error bounds established for neural operators in terms of network parameters.

This paper tackles adaptive control of unknown Markov jump systems with sample complexity and regret bounds.

problem Adaptive control of unknown Markov jump systems with changing dynamics.
method Identification-based adaptive control using a system identification algorithm and certainty equivalent control.
result The proposed adaptive control scheme achieves O(T)\mathcal{O}(\sqrt{T}) regret, improving to O(polylog(T))\mathcal{O}(polylog(T)) with partial knowledge.

ACI uses Bayesian data assimilation to trace causes from effects in complex systems.

problem Capturing instantaneous, time-evolving causal relationships in complex, high-dimensional systems.
method Assimilative causal inference (ACI) leverages Bayesian data assimilation to trace causes backward from observed effects.
result ACI provides online tracking of causal roles that may reverse intermittently and reveals how far effects propagate.

Although classical economic theory is based on the concept of stable equilibrium, real economic systems appear to be always out of equilibrium. Indeed, they share many of the dynamical features of other complex systems, e.g., ecological food-webs. We focus on the relation between increasing complexity of the economic n…

2010-09-06abs ↗pdf ↗

The paper tackles long-context linear system identification with improved sample complexity bounds.

problem Identifying dynamical systems with long dependencies over fixed context windows.
method Established sample complexity bounds for systems with linear dependencies over a context window of length p.
result The learning process is not hindered by slow mixing properties in extended context windows.

Deep reinforcement learning method finds rare events in complex systems.

problem Computing transition pathways in high-dimensional systems.
method Formulated as a cost minimization problem, solved using DDPG with physical properties.
result Efficiently samples and computes globally optimal transition pathways.

A simple guide to understanding hierarchical causality in complex systems.

problem Understanding hierarchical causality in complex systems.
method Formalizing hierarchical causality in terms of actors and agents, with three key structures.
result The system requires three additional structures: causation classes, aggregation operators, and discrete event-time maps.

BLADE uses Bayesian methods to discover complex systems from scarce data.

problem Efficiently discovering governing equations of complex dynamical systems from limited data.
method Combines replica-exchange stochastic gradient Langevin Monte Carlo with active learning.
result Reduces measurement requirements by 60% for Lotka-Volterra and 40% for Burgers' equation.