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The flow table test is an established method used to assess the workability of concrete, particularly useful for evaluating highly flowable concrete mixes. This test employs an apparatus that consists of a wooden board topped with a steel plate, collectively weighing 35 pounds. The board is connected to a base via a hinge and measures 27.6 inches on each side.
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  3. Flowsde: A Flow-matching-based Sde Framework For Predicting State Transitions.
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  3. Flowsde: A Flow-matching-based Sde Framework For Predicting State Transitions.

Related Experiment Video

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
09:49

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Published on: November 18, 2015

FlowSDE: a flow-matching-based SDE framework for predicting state transitions.

Kexin Lou1,2, Yinuo Zhang1, Zhichao Liang1

  • 1Southern University of Science and Technology , Shenzhen, Guangdong, People's Republic of China.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|June 18, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

FlowSDE models complex dynamics using flow matching and physical constraints to identify critical state transitions. This framework accurately predicts transitions and offers a robust solution for analyzing non-equilibrium systems.

Keywords:
SDEcritical transitionepilepsy dynamicsprobabilistic models

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Area of Science:

  • Complex Systems Dynamics
  • Computational Neuroscience
  • Statistical Physics

Background:

  • Stochastic differential equations (SDEs) model diverse phenomena, from chemical reactions to neural dynamics.
  • Identifying critical state transitions in complex systems is crucial for understanding their behavior.
  • Traditional early warning signals can be unreliable under non-stationary or noisy conditions.

Purpose of the Study:

  • To introduce FlowSDE, a novel framework for learning SDEs and modeling non-equilibrium dynamics.
  • To identify critical state transitions and capture system evolution using data-driven representations.
  • To enhance the accuracy and robustness of transition prediction in complex systems.

Main Methods:

  • Utilizing a flow-matching-based SDE learning framework (FlowSDE).
  • Integrating conditional flow matching with physical constraints to model system dynamics.
  • Comparing FlowSDE with traditional early warning signals (variance, lag-1 autocorrelation) in epileptor models.

    • Main Results:

    • FlowSDE accurately uncovers latent transition points and predicts transitions in various dynamical systems.
    • The framework captures mean-field potentials and provides data-driven representations of system evolution.
    • FlowSDE demonstrates robustness and superior performance compared to traditional methods, especially under non-stationary or noisy conditions.

    Conclusions:

    • FlowSDE provides an interpretable, flexible, and robust method for characterizing and forecasting complex dynamics.
    • The framework has broad applications in neuroscience and other fields requiring analysis of critical transitions.
    • This approach offers improved accuracy and reliability for understanding and predicting system state changes.