Turbulent flow simulation using autoregressive conditional diffusion models
Simulating turbulent flows is crucial for a wide range of applications, and machine learning-
based solvers are gaining increasing relevance. However, achieving stability when …
based solvers are gaining increasing relevance. However, achieving stability when …
A meta-model to predict the drag coefficient of a particle translating in viscoelastic fluids: a machine learning approach
This study presents a framework based on Machine Learning (ML) models to predict the
drag coefficient of a spherical particle translating in viscoelastic fluids. For the purpose of …
drag coefficient of a spherical particle translating in viscoelastic fluids. For the purpose of …
Combining federated learning and control: A survey
This survey provides an overview of combining federated learning (FL) and control to
enhance adaptability, scalability, generalization, and privacy in (nonlinear) control …
enhance adaptability, scalability, generalization, and privacy in (nonlinear) control …
Unsteady reduced order model with neural networks and flight-physics-based regularization for aerodynamic applications
MD Ribeiro, M Stradtner, P Bekemeyer - Computers & Fluids, 2023 - Elsevier
Numerical simulation of unsteady fluid flow plays an important role in several areas of the
aeronautical industry. Since high-fidelity computational fluid dynamics simulations could be …
aeronautical industry. Since high-fidelity computational fluid dynamics simulations could be …
Modelling membrane curvature generation using mechanics and machine learning
The deformation of cellular membranes regulates trafficking processes, such as exocytosis
and endocytosis. Classically, the Helfrich continuum model is used to characterize the forces …
and endocytosis. Classically, the Helfrich continuum model is used to characterize the forces …
A PINN-based level-set formulation for reconstruction of bubble dynamics
Solving problems in fluid mechanics plays an essential role in science and engineering,
especially when it comes to optimal design, reconstruction of biomedical and geophysical …
especially when it comes to optimal design, reconstruction of biomedical and geophysical …
Bayesian Reasoning for Physics Informed Neural Networks
KM Graczyk, K Witkowski - arxiv preprint arxiv:2308.13222, 2023 - arxiv.org
Physics informed neural network (PINN) approach in Bayesian formulation is presented. We
adopt the Bayesian neural network framework formulated by MacKay (Neural Computation 4 …
adopt the Bayesian neural network framework formulated by MacKay (Neural Computation 4 …
Integrating Physics-Informed Deep Learning and Numerical Methods for Robust Dynamics Discovery and Parameter Estimation
C Ho, A Arnold - arxiv preprint arxiv:2410.04299, 2024 - arxiv.org
Incorporating a priori physics knowledge into machine learning leads to more robust and
interpretable algorithms. In this work, we combine deep learning techniques and classic …
interpretable algorithms. In this work, we combine deep learning techniques and classic …
Finding the Underlying Viscoelastic Constitutive Equation via Universal Differential Equations and Differentiable Physics
This research employs Universal Differential Equations (UDEs) alongside differentiable
physics to model viscoelastic fluids, merging conventional differential equations, neural …
physics to model viscoelastic fluids, merging conventional differential equations, neural …
Graph Spring Neural ODEs for Link Sign Prediction
A Rehmann, A Bovet - arxiv preprint arxiv:2412.12916, 2024 - arxiv.org
Signed graphs allow for encoding positive and negative relations between nodes and are
used to model various online activities. Node representation learning for signed graphs is a …
used to model various online activities. Node representation learning for signed graphs is a …