Transfer learning for nonlinear dynamics and its application to fluid turbulence
We introduce transfer learning for nonlinear dynamics, which enables efficient predictions of chaotic dynamics by utilizing a small amount of data. For the Lorenz chaos, by optimizing the transfer rate, we accomplish more accurate inference than the conventional method by an order of magnitude. Moreover, a surprisingly small amount of learning is enough to infer the energy dissipation rate of the Navier-Stokes turbulence because we can, thanks to the small-scale universality of turbulence, transfer a large amount of the knowledge learned from turbulence data at lower Reynolds number.
Code (0)
등록된 구현이 없습니다.
Tasks
Transfer LearningSimilar Papers 제목 키워드 기반
A fully-differentiable compressible high-order computational fluid dynamics solver
Fluid flows are omnipresent in nature and engineering disciplines. The reliable computation of fluids has been a long-lasting challenge due to nonlinear interactions over multiple spatio-temporal scales. The compressible…
Vocal Bursts Intensity PredictionShape Invariant 3D-Variational Autoencoder: Super Resolution in Turbulence flow
Deep learning provides a versatile suite of methods for extracting structured information from complex datasets, enabling deeper understanding of underlying fluid dynamic phenomena. The field of turbulence modeling, in p…
JAX-FLUIDS: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows
Physical systems are governed by partial differential equations (PDEs). The Navier-Stokes equations describe fluid flows and are representative of nonlinear physical systems with complex spatio-temporal interactions. Flu…
Embedding Hard Physical Constraints in Convolutional Neural Networks for 3D Turbulence
Deep learning approaches have shown much promise for physical sciences, especially in dimensionality reduction and compression of large datasets. A major issue in deep learning of large-scale phenomena, like fluid turbul…
Deep LearningDimensionality ReductionInductive BiasAdditive-feature-attribution methods: a review on explainable artificial intelligence for fluid dynamics and heat transfer
The use of data-driven methods in fluid mechanics has surged dramatically in recent years due to their capacity to adapt to the complex and multi-scale nature of turbulent flows, as well as to detect patterns in large-sc…
Explainable artificial intelligence