Ottica

(KtWVvaf.png (835×951)) via Ivan Safrin

Shiv Integer

Automated Art bot constructs new sculptural designs by collaging several free-use Creative Commons 3D models together into semi-abstract forms which can be downloaded and 3D printed as readymade sculptures:

“You are all even more beautiful together.”

Physics Forests

Computer graphics research from ETH Zurich produces efficient and realistic fluid dydnamics simulations using machine learning (and kinda unintentionally creates Net Art in the process a la @fruitsoftheweb :

Traditional fluid simulations require large computational resources even for an average sized scene with the main bottleneck being a very small time step size, required to guarantee the stability of the solution. Despite a large progress in parallel computing and efficient algorithms for pressure computation in the recent years, realtime fluid simulations have been possible only under very restricted conditions. In this paper we propose a novel machine learning based approach, that formulates physics-based fluid simulation as a regression problem, estimating the acceleration of every particle for each frame. We designed a feature vector, directly modelling individual forces and constraints from the Navier-Stokes equations, giving the method strong generalization properties to reliably predict positions and velocities of particles in a large time step setting on yet unseen test videos. We used a regression forest to approximate the behaviour of particles observed in the large training set of simulations obtained using a traditional solver. Our GPU implementation led to a speed-up of one to three orders of magnitude compared to the state-of-the-art position-based fluid solver and runs in real-time for systems with up to 2 million particles.