AI-Lorenz: A physics-data-driven framework for black-box and gray-box identification of chaotic systems with symbolic regression
Discovering mathematical models that characterize the observed behavior of dynamical
systems remains a major challenge, especially for systems in a chaotic regime, due to their …
systems remains a major challenge, especially for systems in a chaotic regime, due to their …
Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning
The high dimensionality and complex dynamics of turbulent flows remain an obstacle to the
discovery and implementation of control strategies. Deep reinforcement learning (RL) is a …
discovery and implementation of control strategies. Deep reinforcement learning (RL) is a …
[HTML][HTML] Data-driven reduced-order modeling of spatiotemporal chaos with neural ordinary differential equations
Dissipative partial differential equations that exhibit chaotic dynamics tend to evolve to
attractors that exist on finite-dimensional manifolds. We present a data-driven reduced-order …
attractors that exist on finite-dimensional manifolds. We present a data-driven reduced-order …
A multifidelity deep operator network approach to closure for multiscale systems
Projection-based reduced order models (PROMs) have shown promise in representing the
behavior of multiscale systems using a small set of generalized (or latent) variables. Despite …
behavior of multiscale systems using a small set of generalized (or latent) variables. Despite …
Divide and conquer: Learning chaotic dynamical systems with multistep penalty neural ordinary differential equations
Forecasting high-dimensional dynamical systems is a fundamental challenge in various
fields, such as geosciences and engineering. Neural Ordinary Differential Equations …
fields, such as geosciences and engineering. Neural Ordinary Differential Equations …
On robustness of neural ODEs image classifiers
Abstract Neural Ordinary Differential Equations (Neural ODEs), as a family of novel deep
models, delicately link conventional neural networks and dynamical systems, which bridges …
models, delicately link conventional neural networks and dynamical systems, which bridges …
Physics-agnostic and physics-infused machine learning for thin films flows: modelling, and predictions from small data
CP Martin-Linares, YM Psarellis… - Journal of Fluid …, 2023 - cambridge.org
Numerical simulations of multiphase flows are crucial in numerous engineering applications,
but are often limited by the computationally demanding solution of the Navier–Stokes (NS) …
but are often limited by the computationally demanding solution of the Navier–Stokes (NS) …
Dynamics of a data-driven low-dimensional model of turbulent minimal Couette flow
Because the Navier–Stokes equations are dissipative, the long-time dynamics of a flow in
state space are expected to collapse onto a manifold whose dimension may be much lower …
state space are expected to collapse onto a manifold whose dimension may be much lower …
Neural dynamical operator: Continuous spatial-temporal model with gradient-based and derivative-free optimization methods
Data-driven modeling techniques have been explored in the spatial-temporal modeling of
complex dynamical systems for many engineering applications. However, a systematic …
complex dynamical systems for many engineering applications. However, a systematic …
Deep learning delay coordinate dynamics for chaotic attractors from partial observable data
A common problem in time-series analysis is to predict dynamics with only scalar or partial
observations of the underlying dynamical system. For data on a smooth compact manifold …
observations of the underlying dynamical system. For data on a smooth compact manifold …