22 citations · 48 across the 13 of their papers we have counts for
13 papers
Inverse problems with diffusion models: MAP estimation via mode-seeking loss
Sai Bharath Chandra Gutha, Ricardo Vinuesa, Hossein Azizpour
A pre-trained unconditional diffusion model, combined with posterior sampling or maximum a posteriori (MAP) estimation techniques, can solve arbitrary inverse problems without task…
Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environments
Abhijeet Vishwasrao, Sai Bharath Chandra Gutha, Andres Cremades +8
Rapid urbanization demands efficient monitoring of turbulent wind and pollutant dispersion, yet existing reconstruction and sensor placement strategies fail under realistic sparsit…
Revisiting Score Function Estimators for -Subset Sampling
Klas Wijk, Ricardo Vinuesa, Hossein Azizpour
Are score function estimators an underestimated approach to learning with -subset sampling? Sampling -subsets is a fundamental operation in many machine learning tasks that i…
Inverse Problems with Diffusion Models: A MAP Estimation Perspective
Sai Bharath Chandra Gutha, Ricardo Vinuesa, Hossein Azizpour
Inverse problems have many applications in science and engineering. In Computer vision, several image restoration tasks such as inpainting, deblurring, and super-resolution can be…
Decoding complexity: how machine learning is redefining scientific discovery
Ricardo Vinuesa, Paola Cinnella, Jean Rabault +10
As modern scientific instruments generate vast amounts of data and the volume of information in the scientific literature continues to grow, machine learning (ML) has become an ess…
Indirectly Parameterized Concrete Autoencoders
Alfred Nilsson, Klas Wijk, Sai bharath chandra Gutha +7
Feature selection is a crucial task in settings where data is high-dimensional or acquiring the full set of features is costly. Recent developments in neural network-based embedded…
Drag-reduction strategies in wall-bounded turbulent flows using deep reinforcement learning
L. Guastoni, J. Rabault, H. Azizpour +1
In this work we compare different drag-reduction strategies that compute their actuation based on the fluctuations at a given wall-normal location in turbulent open channel flow. I…
Predicting the wall-shear stress and wall pressure through convolutional neural networks
Arivazhagan G. Balasubramanian, Luca Guastoni, Philipp Schlatter +2
The objective of this study is to assess the capability of convolution-based neural networks to predict wall quantities in a turbulent open channel flow. The first tests are perfor…
Deep reinforcement learning for turbulent drag reduction in channel flows
L. Guastoni, J. Rabault, P. Schlatter +2
We introduce a reinforcement learning (RL) environment to design and benchmark control strategies aimed at reducing drag in turbulent fluid flows enclosed in a channel. The environ…
Fully convolutional networks for velocity-field predictions based on the wall heat flux in turbulent boundary layers
L. Guastoni, A. G. Balasubramanian, F. Foroozan +6
Fully-convolutional neural networks (FCN) were proven to be effective for predicting the instantaneous state of a fully-developed turbulent flow at different wall-normal locations…
The potential of artificial intelligence for achieving healthy and sustainable societies
B. Sirmacek, S. Gupta, F. Mallor +10
In this chapter we extend earlier work (Vinuesa et al., Nature Communications 11, 2020) on the potential of artificial intelligence (AI) to achieve the 17 Sustainable Development G…
Predicting the near-wall region of turbulence through convolutional neural networks
A. G. Balasubramanian, L. Guastoni, A. Güemes +5
Modelling the near-wall region of wall-bounded turbulent flows is a widespread practice to reduce the computational cost of large-eddy simulations (LESs) at high Reynolds number. A…
Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks
L. Guastoni, M. P. Encinar, P. Schlatter +2
A fully-convolutional neural-network model is used to predict the streamwise velocity fields at several wall-normal locations by taking as input the streamwise and spanwise wall-sh…