arXiv research

The paper models market crashes as phase transitions, finding dynamic transitions offer better predictions.

ISOKANN learns collective variables and effective dynamics for metastable transitions.

New algorithm reduces performance loss in IRL with mismatched transition dynamics.

Study models forest transitions with deep learning for parameter estimation.

Study analyzes stock market dynamics using recurrence measures and transitions.

We present a continuous-time maximum likelihood estimation methodology for credit rating transition probabilities, taking into account the presence of censored data. We perform rolling estimates of the transition matrices with exponential time weighting with varying horizons and discuss the underlying dynamics of trans…

TMTF improves time series visualization by separating dynamic regimes.

This research improves dynamical systems understanding by identifying latent states and their nonlinear transitions.

New transitions found in Spin(7) holonomy metrics related to a dynamical system.

In this paper we study how to learn stochastic, multimodal transition dynamics in reinforcement learning (RL) tasks. We focus on evaluating transition function estimation, while we defer planning over this model to future work. Stochasticity is a fundamental property of many task environments. However, discriminative f…

In an adaptive population which models financial markets and distributed control, we consider how the dynamics depends on the diversity of the agents' initial preferences of strategies. When the diversity decreases, more agents tend to adapt their strategies together. This change in the environment results in dynamical…

Modeling poverty transitions in India over 54 years, showing rising but persistent poverty.

Bayesian method estimates dynamics from near-optimal trajectories.

A new method for ILO with transition model disparity using an intermediary policy.

New methods use machine learning to simulate rare transitions in molecular systems.

At the heart of technology transitions lie complex processes of social and industrial dynamics. The quantitative study of sustainability transitions requires modelling work, which necessitates a theory of technology substitution. Many, if not most, contemporary modelling approaches for future technology pathways overlo…

New algorithm reduces dynamic regret for MDPs with unknown transition and adversarial rewards.

New method for state inference in state-space models with unknown dynamics.

GDM models time series with smoother transitions and interpretable states.

SCPO learns robust policies without modeling disturbance, improving real-world task performance.

The paper studies dynamical systems with evolving geometric structure using numerical methods.

Enhances neural network dynamics to boost computational capacity.

A new method identifies critical transitions in high-dimensional data.

Paper tackles dynamic behavior of variable topology mechanisms, presenting new transition conditions.

A framework combining HSMM and survival analysis for lifecycle-oriented mobility analysis.

We study the phase transition of dynamical herd behaviors for the yen-dollar exchange rate in the Japanese financial market. It is obtained that the probability distribution of returns satisfies the power-law behavior with three different values of the scaling exponent 3.11 (one time lag $τ$ = 1 minute), 2.81 (30 minut…

Study shows climate change can cause a 'run on fossil fuels' affecting prices and production.

Study of transitivity in partially hyperbolic maps with expanding linear part.

We study the crash dynamics of the Warsaw Stock Exchange (WSE) by using the Minimal Spanning Tree (MST) networks. We find the transition of the complex network during its evolution from a (hierarchical) power law MST network, representing the stable state of WSE before the recent worldwide financial crash, to a superst…

We focus on variational inference in dynamical systems where the discrete time transition function (or evolution rule) is modelled by a Gaussian process. The dominant approach so far has been to use a factorised posterior distribution, decoupling the transition function from the system states. This is not exact in gene…

Novel approach detects early warning indicators in complex systems.

TOLD++ improves convergence of diffusion models by critically damping the forward transition matrix.

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference \emph{and learning} (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gauss…

We give a polynomial-time algorithm for learning latent-state linear dynamical systems without system identification, and without assumptions on the spectral radius of the system's transition matrix. The algorithm extends the recently introduced technique of spectral filtering, previously applied only to systems with a…

The paper investigates non-linear and heavy-tailed predictability in transition-energy financial markets.

This paper presents a general theory that aims at explaining timescales observed empirically in technology transitions and predicting those of future transitions. This framework is used further to derive a theory for exploring the dynamics that underlie the complex phenomenon of irreversible and path dependent price or…

New model explains market dynamics with phase transitions and non-linear interactions.

Develops methods to simulate rare transitions in molecular systems.

We study the problem of predicting rare critical transition events for a class of slow-fast nonlinear dynamical systems. The state of the system of interest is described by a slow process, whereas a faster process drives its evolution and induces critical transitions. By taking advantage of recent advances in reservoir…

Adaptive populations such as those in financial markets and distributed control can be modeled by the Minority Game. We consider how their dynamics depends on the agents' initial preferences of strategies, when the agents use linear or quadratic payoff functions to evaluate their strategies. We find that the fluctuatio…

The goal of system identification is to learn about underlying physics dynamics behind the time-series data. To model the probabilistic and nonparametric dynamics model, Gaussian process (GP) have been widely used; GP can estimate the uncertainty of prediction and avoid over-fitting. Traditional GPSSMs, however, are ba…

New CVs preserve transition rates in molecular dynamics.

TempLe learns transition templates for efficient multi-task RL.

Study analyzes order transitions in high, medium, and low market cap stocks using Markov chains.

We study the maximum mean discrepancy (MMD) in the context of critical transitions modelled by fast-slow stochastic dynamical systems. We establish a new link between the dynamical theory of critical transitions with the statistical aspects of the MMD. In particular, we show that a formal approximation of the MMD near …

Combines deep state space models with diffusion models for better forecasting and capturing latent dynamics

New state-only IL algorithm tackles MDP transition mismatch.

LoRA fine-tuning causes forgetting, studied via particle system dynamics.