Powered by Computational Fluid Dynamics Research + Deep Learning

Physics Simulation
Made Beautiful

Generate stunning, physically-accurate fluid animations in seconds. Built on deep learning research in computational fluid dynamics, our AI corrects numerical errors in real-time for unprecedented accuracy.

Why FluidMotion?

The first tool that combines real physics simulation with AI correction, making professional-quality fluid animations accessible to everyone.

Real Physics, Not Tricks

Our simulation engine uses MPS/MPH (Moving Particle Hydrodynamics), the same class of particle methods used in ocean engineering, tsunami modeling, and aerospace research. Every animation is physically accurate.

AI-Corrected Accuracy

Our deep learning model detects and corrects numerical energy dissipation in real-time. Trained on physics simulation data, it achieves 2.4x improvement in wave amplitude preservation over traditional methods.

Interactive & Exportable

Touch and interact with the simulation in real-time. Customize colors, physics parameters, and rendering styles. Export as high-quality video, GIF, or embeddable code.

Serious Research
Behind Every Pixel

FluidMotion is built on peer-reviewed research in computational fluid dynamics and deep learning, developed at the University of Tokyo. Our core technology solves a fundamental challenge: suppressing numerical wave amplitude attenuation in particle-based simulations.

MPS/MPH Particle Method

Industry-standard computational fluid dynamics technique deployed in tsunami modeling, dam break analysis, offshore engineering, and aerospace.

Deep Learning Correction Model

A neural network trained via imitation learning predicts correction accelerations from 16-dimensional physics features per particle, achieving 2.4x improvement in long-term wave preservation.

Real-Time Browser Engine

Optimized MPS engine running directly in your browser with spatial hashing for O(N) neighbor search and GPU-accelerated rendering.

mps-engine.ts
// AI Correction Engine
// Input: 16D physics features per particle
//   - position, velocity, density, pressure
//   - velocity history (5 timesteps)
//   - neighborhood statistics
// Output: correction acceleration (2D)

applyMLCorrection() {
  for (particle of fluidParticles) {
    features = extractFeatures(particle)
    correction = neuralNetwork(features)
    particle.velocity += correction * dt
  }
}

// Result: 43.5% wave height preserved
//   vs 18.4% without correction

Start Creating

From free exploration to professional production.

Free

$0/month
  • 3 exports / month
  • 720p resolution
  • Watermark
  • 4 color themes
  • Community support
Get Started

Pro

$19/month
  • Unlimited exports
  • 4K resolution
  • No watermark
  • Custom colors
  • Priority support
  • API access
Start Free Trial

Enterprise

$99/month
  • Everything in Pro
  • White-label
  • Custom physics presets
  • Dedicated support
  • On-premise deployment
  • SLA guarantee
Contact Sales