A practical approach to flow field reconstruction with sparse or incomplete data through physics informed neural network
High-resolution flow field reconstruction is prevalently recognized as a difficult task in the
field of experimental fluid mechanics, since the measured data are usually sparse and …
field of experimental fluid mechanics, since the measured data are usually sparse and …
Deep learning methods for partial differential equations and related parameter identification problems
Recent years have witnessed a growth in mathematics for deep learning—which seeks a
deeper understanding of the concepts of deep learning with mathematics and explores how …
deeper understanding of the concepts of deep learning with mathematics and explores how …
Gaussian process priors for systems of linear partial differential equations with constant coefficients
M Harkonen, M Lange-Hegermann… - … on machine learning, 2023 - proceedings.mlr.press
Partial differential equations (PDEs) are important tools to model physical systems and
including them into machine learning models is an important way of incorporating physical …
including them into machine learning models is an important way of incorporating physical …
Conditional neural field latent diffusion model for generating spatiotemporal turbulence
Eddy-resolving turbulence simulations are essential for understanding and controlling
complex unsteady fluid dynamics, with significant implications for engineering and scientific …
complex unsteady fluid dynamics, with significant implications for engineering and scientific …
Constraining Gaussian processes to systems of linear ordinary differential equations
A Besginow… - Advances in Neural …, 2022 - proceedings.neurips.cc
Data in many applications follows systems of Ordinary Differential Equations (ODEs). This
paper presents a novel algorithmic and symbolic construction for covariance functions of …
paper presents a novel algorithmic and symbolic construction for covariance functions of …
Bayesian conditional diffusion models for versatile spatiotemporal turbulence generation
Turbulent flows, characterized by their chaotic and stochastic nature, have historically
presented formidable challenges to predictive computational modeling. Traditional eddy …
presented formidable challenges to predictive computational modeling. Traditional eddy …
A probabilistic digital twin for leak localization in water distribution networks using generative deep learning
Localizing leakages in large water distribution systems is an important and ever-present
problem. Due to the complexity originating from water pipeline networks, too few sensors …
problem. Due to the complexity originating from water pipeline networks, too few sensors …
From zero to turbulence: Generative modeling for 3d flow simulation
Simulations of turbulent flows in 3D are one of the most expensive simulations in
computational fluid dynamics (CFD). Many works have been written on surrogate models to …
computational fluid dynamics (CFD). Many works have been written on surrogate models to …
A denoising diffusion model for fluid field prediction
G Yang, S Sommer - arXiv preprint arXiv:2301.11661, 2023 - arxiv.org
We propose a novel denoising diffusion generative model for predicting nonlinear fluid fields
named FluidDiff. By performing a diffusion process, the model is able to learn a complex …
named FluidDiff. By performing a diffusion process, the model is able to learn a complex …
Physics-enhanced deep surrogates for partial differential equations
Many physics and engineering applications demand partial differential equations (PDE)
property evaluations that are traditionally computed with resource-intensive high-fidelity …
property evaluations that are traditionally computed with resource-intensive high-fidelity …