arXiv research

The paper offers error bounds for quantized dynamical models.

A method for identifying NPWARX models with arbitrary domains using probabilistic mixture models.

Paper develops a framework to identify latent dynamics from high-dimensional data.

PhICNet combines physics and deep learning for forecasting and source identification in dynamical systems.

The paper provides global optimization algorithms for two particularly difficult nonconvex problems raised by hybrid system identification: switching linear regression and bounded-error estimation. While most works focus on local optimization heuristics without global optimality guarantees or with guarantees valid only…

Enhances RSCNs with hybrid regularization for nonlinear dynamics.

Paper tackles counterfactual sentence detection and evaluation.

Hybrid AI and rule-based framework de-identifies medical imaging data.

Estimates hybrid dynamical systems with polynomial expansions and Markovian switching.

Breaks down complex nonlinear dynamics into simpler components.

The identification of sources of advection-diffusion transport is based usually on solving complex ill-posed inverse models against the available state- variable data records. However, if there are several sources with different locations and strengths, the data records represent mixtures rather than the separate influ…

The paper provides results regarding the computational complexity of hybrid system identification. More precisely, we focus on the estimation of piecewise affine (PWA) maps from input-output data and analyze the complexity of computing a global minimizer of the error. Previous work showed that a global solution could b…

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

Defines hybrid systems on principal bundles and studies impact effects.

Safe RL-based vibration control using LQR guidance.

Physics-informed neural networks improve model accuracy and efficiency.

EnKO combines VI and EnKF for efficient latent dynamics inference.

Hybrid systems are characterized by having an interaction between continuous dynamics and discrete events. The contribution of this paper is to provide hybrid systems with a novel geometric formulation so that controls can be added. Using this framework we describe some new global controllability tests for hybrid contr…

Expert augmentation improves hybrid model generalization.

A novel deep probabilistic model for dynamic systems forecasting.

Hybrid model improves traffic flow prediction accuracy.

In this work we present a new approach on studying dynamical systems. Combining the two ways of expressing the uncertainty, using probabilistic theory and credibility theory, we have research the generalized fractional hybrid equations. We have introduced the concepts of generalized fractional Wiener process, generaliz…

When the Poincaré map associated with a periodic orbit of a hybrid dynamical system has constant-rank iterates, we demonstrate the existence of a constant-dimensional invariant subsystem near the orbit which attracts all nearby trajectories in finite time. This result shows that the long-term behavior of a hybrid model…

In financial field, a robust software system is of vital importance to ensure the smooth operation of financial transactions. However, many financial corporations still depend on operators to identify and eliminate the system failures when financial software systems break down. This traditional operation method is time…

In the present paper we study interval identification systems of order three. We prove that the Rauzy induction preserves symmetry: for any symmetric interval identification system of order three after finitely many iterations of the Rauzy induction we always obtain a symmetric system. We also provide an example of sym…

Paper explores using EEG for better speaker identification, even in noisy environments.

A distributed system identification method for LTI systems using reverse experience replay.

dynoGP uses deep Gaussian processes for dynamic system identification.

Modeling dynamical systems is important in many disciplines, e.g., control, robotics, or neurotechnology. Commonly the state of these systems is not directly observed, but only available through noisy and potentially high-dimensional observations. In these cases, system identification, i.e., finding the measurement map…

Bayesian neural networks with nonparametric noise models for system identification.

Paper proposes hybrid machine learning for tuning first principles models in engineering systems.

Hybrid model avoids forgetting across tasks and classes.

Researchers prove inequalities for reaction-diffusion systems using a new curvature-dimension condition.

A hybrid ASR system using conformer architecture improves word-error-rate and training speed.

Hybrid model predicts flow and pressure in water systems.

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

Stablecoins are reshaping global monetary systems, offering hybrid structures with public and private monies.

Study identifies and validates a method for system identification of Markov jump linear systems.

This paper tackles Bayesian system identification with probabilistic numerical methods.

Proposes a deep hybrid model for better recommendation systems.

Deep SSMs use neural networks to identify complex systems.

There is an implicit assumption that traditional hybrid approaches for automatic speech recognition (ASR) cannot directly model graphemes and need to rely on phonetic lexicons to get competitive performance, especially on English which has poor grapheme-phoneme correspondence. In this work, we show for the first time t…

A tutorial on non-asymptotic system identification methods.

Semi-parametric framework for nonlinear system identification

This paper provides a geometrical derivation of the Hybrid Minimum Principle (HMP) for autonomous hybrid systems whose state manifolds constitute Lie groups $(G,\star)$ which are left invariant under the controlled dynamics of the system, and whose switching manifolds are defined as smooth embedded time invariant subma…

Equation discovery methods enable modelers to combine domain-specific knowledge and system identification to construct models most suitable for a selected modeling task. The method described and evaluated in this paper can be used as a nonlinear system identification method for gray-box modeling. It consists of two int…

dynoNet learns dynamical systems using linear operators.

A fast method estimates correlations in hybrid systems using observable market data.