KTH-FlowAI

9eqModel_KNFandLSTM

beta-Variational-autoencoders-and-transformers-for-reduced-order-modelling-of-fluid-flows

causality-urban-flows

DeepReinforcementLearning_RayleighBenard2D_Control

Control of 2D Rayleigh Benard Convection using Deep Reinforcement Learning with Tensorforce and Shenfun.

FCN-turbulence-predictions-from-wall-quantities

Prediction of the velocity flow fields at a given distance from wall, starting from wall-measured quantities in wall-bounded turbulence

Identifying-regions-of-importance-in-wall-bounded-turbulence-through-explainable-deep-learning

Code for reproducing the article: Identifying regions of importance in wall-bounded turbulence through explainable deep learning.

MARL-drag-reduction-in-wall-bounded-flows

A multi-agent reinforcement learning environment to design and benchmark control strategies aimed at reducing drag in turbulent open channel flow

Physics-informed-deep-learning-applications-to-experimental-fluid-mechanics

Physics-informed-neural-networks-for-solving-Reynolds-averaged-Navier-Stokes-equations

Towards-extraction-of-orthogonal-and-parsimonious-non-linear-modes-from-turbulent-flows

KTH-FlowAI/Physics-informed-neural-networks-for-solving-Reynolds-averaged-Navier-Stokes-equations

KTH-FlowAI/MARL-drag-reduction-in-wall-bounded-flows

A multi-agent reinforcement learning environment to design and benchmark control strategies aimed at reducing drag in turbulent open channel flow

KTH-FlowAI/beta-Variational-autoencoders-and-transformers-for-reduced-order-modelling-of-fluid-flows

KTH-FlowAI/FCN-turbulence-predictions-from-wall-quantities

Prediction of the velocity flow fields at a given distance from wall, starting from wall-measured quantities in wall-bounded turbulence

KTH-FlowAI/DeepReinforcementLearning_RayleighBenard2D_Control

Control of 2D Rayleigh Benard Convection using Deep Reinforcement Learning with Tensorforce and Shenfun.

KTH-FlowAI/Towards-extraction-of-orthogonal-and-parsimonious-non-linear-modes-from-turbulent-flows

KTH-FlowAI/Physics-informed-deep-learning-applications-to-experimental-fluid-mechanics

KTH-FlowAI/9eqModel_KNFandLSTM

KTH-FlowAI/Identifying-regions-of-importance-in-wall-bounded-turbulence-through-explainable-deep-learning

Code for reproducing the article: Identifying regions of importance in wall-bounded turbulence through explainable deep learning.

KTH-FlowAI/Beta-VAE-combined-with-Transformers-for-ROM

KTH-FlowAI/causality-urban-flows

KTH-FlowAI/Enhancement-of-PIV-via-PINNs

Enhancement of PIV measurements via physics-informed neural networks

KTH-FlowAI/AdvancedDeepReinforcementLearning_RBC_Control

MARL for the control of 2D RBC with G-CNNs and positional encodings

KTH-FlowAI/DeepTurbulence

KTH-FlowAI/DNS-ZPG-TBL-with-passive-scalars-up-to-Pr6

KTH-FlowAI/DRL_MARL_RayleighBenard3D_Control

Deep Reinforcement Learning (DRL) for the control of three-dimensional (3D) Rayleigh-Bénard convection (RBC) using the multi-agent reinforcement learning (MARL) framework

KTH-FlowAI/Sentiment-analysis-on-Twitter-data-towards-climate-action