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Accelerating Eulerian Fluid Simulation With Convolutional Networks

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Jonathan Tompson from Google and his colleagues, Kristofer Schlachter, Pablo Sprechmann and Ken Perlin from the New York University have come up with a really nice technique to teach a convolutional neural network how fluid and smoke simulations work. The project webpage is available here, or you can click on the image below to access the paper.

Status: accepted to ICML 2017

(please note that I am in the acknowledgements section and was not a co-author of the paper)

cnnfluid-cover

Two Minute Papers – Deep Neural Network Learns Van Gogh’s Art

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Artificial neural networks were inspired by the human brain and simulate how neurons behave when they are shown a sensory input (e.g., images, sounds, etc). They are known to be excellent tools for image recognition, any many other problems beyond that – they also excel at weather predictions, breast cancer cell mitosis detection, brain image segmentation and toxicity prediction among many others. Deep learning means that we use an artificial neural network with multiple layers, making it even more powerful for more difficult tasks.

This time they have been shown to be apt at reproducing the artistic style of many famous painters, such as Vincent Van Gogh and Pablo Picasso among many others. All the user needs to do is provide an input photograph and a target image from which the artistic style will be learned.