11/2/2023 0 Comments Fluid simulation games![]() Any problem? Write to me and I will help. Constructive comments allow us to create even better projects. fluid-simulation grmhd neutron-stars einstein-toolkit bns-mergers. Also included are several examples for running BNS merger sims. We try to make each project unique, introducing something new on the screen. This includes an extension of the IllinoisGRMHD code (to allow the use of finite temperature EOS tables), along with several supporting codes which work within the EinsteinToolkit and Cactus infrastructure. We are a tiny team creating interactive 3D wallpapers for Windows 10/11. The project will be developed so there will probably be more options soon. Multi-Language - the menu with many languages. Optimized code created for mobile devices. ![]() Optimization - reduced equipment requirements. JPG)Īutomatic and manual Pause Mode - will pause wallpaper when you need 100% power for your games. Own text - add your own text to the wallpaper. Many editing options - you can change many parameters. Ready presets - a few presets made by us but you can add your own. Main features:Interactive simulation - Fluid that reacts to the mouse movement and clicking. By default, for 3DM you can also add your own text, logos or clock. We have prepared some ready settings for you but you can also add your own (presets) to quickly change them. In options, you can change many parameters that change the appearance and behaviour of the fluid. We may be able to transmit the movement of water over the internet in real time so that even those far away can experience the same lifelike water motion.Our latest project Fluid Engine presents a beautiful fluid simulation that responds to your mouse. "This technology will enable the creation of VR games where you can control things using water and actually feel the water in the game. Kitamura stresses that the technology will make VR more immersive and improve online communication. Thanks to the developed magnetic motion capture and flow reconstruction technique, real-time 3D flow measurement is now possible. But it still remained difficult to measure 3D flow in real-time, especially when the liquid was in an opaque container or was opaque itself. Previous techniques had typically tracked tiny particles suspended inside the liquid with cameras. The computer then refines the way of pushing via deep reinforcement learning. Then, they made each buoy act like a force that pushes the simulated liquid, making it flow like real liquid. Deep reinforcement learning combines reinforcement learning with deep neural networks to solve complex problems.įirst, the researchers used a computer to simulate calm liquid. A computer performs actions, receives feedback (reward or punishment) from its environment, and then adjusts its future actions to maximize its total rewards over time, much like a dog associates treats with good behavior. Reinforcement learning is the trial-and-error process through which learning takes place. "We overcame this by combining a fluid simulation with deep reinforcement learning to perform the recovery," says Yoshifumi Kitamura, deputy director of RIEC. The crucial step involved finding an innovative solution to recovering the detailed water motion from the movement of a few buoys. The movement of each buoy could then be tracked using a magnetic motion capture system. To collect flow data, the group - which comprised researchers from Tohoku University's Research Institute of Electrical Communication (RIEC) and the Institute of Fluid Science - placed buoys embedded with special magnetic markers on water. The technology opens up the possibility for virtual reality interactions involving water.ĭetails of their findings were published in the journal ACM Transactions on Graphics on September 17, 2023.Ĭrucial to the breakthrough was creating both a flow measurement technique and a flow reconstruction method that replicated agitated liquid motion. Replicating this agitated liquid motion, as it is known, allowed them to recreate water flow in real time based on only a small amount of data from real water. Now, a research team from Tohoku University has harnessed the power of deep reinforcement learning to replicate the flow of water when disturbed.
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