From Simulation to Prediction: Build an AI Model that Learns Fluid Dynamics

What if AI could predict how water flows without solving complex physics equations?

Traditional Computational Fluid Dynamics (CFD) simulations can take minutes, hours, or even days depending on the problem. Engineers rely on these simulations to design aircraft, cars, wind turbines, weather models, and even medical devices.

But what if an AI model could learn those patterns and make predictions in milliseconds?

In this hands-on tutorial, we’ll explore WALRUS, a physics foundation model developed by PolymathicAI for fluid dynamics, and build a simple project that predicts future fluid motion from previous observations.

By the end of this guide, you’ll:

• Understand why AI is transforming scientific computing.
• Learn what a foundation model for physics looks like.
• Run inference using a pre-trained model.
• Visualize fluid predictions.
• Discover where this technology is already making an impact.

Research PDF

A formal research-style PDF version of this article is available here: 

From Simulation to Prediction: Data-Driven Modeling of Fluid Dynamics Using Artificial Intelligence

Suggested citation:
Pruseth, D. (2026). From Simulation to Prediction: Data-Driven Modeling of Fluid Dynamics Using Artificial Intelligence. Debabrata Pruseth AI Blog.

The Problem

Imagine designing a new aircraft wing.

Before manufacturing begins, engineers simulate thousands of airflow conditions. Every simulation solves millions of mathematical equations describing how air moves around the wing.

These equations are based on the Navier–Stokes equations, which accurately describe fluid motion but are computationally expensive.

As simulations become larger, computation costs grow rapidly.

This is where AI becomes interesting.

Instead of solving the equations from scratch every time, an AI model can learn the relationship between previous fluid states and future states.

Think of it like weather forecasting.

Instead of calculating every atmospheric interaction from first principles, AI learns patterns from enormous amounts of historical data.

Why This Matters

Scientific AI is becoming one of the fastest-growing areas of machine learning.

Researchers are now building foundation models not just for language, but for:

• Weather prediction
• Climate modeling
• Fluid dynamics
• Molecular simulations
• Material discovery
• Protein folding

These models are changing how scientific simulations are performed.

Meet WALRUS

WALRUS, a physics foundation model developed by PolymathicAI.

Rather than being trained on text, it is trained on thousands of fluid simulations.

Its goal is simple:

Predict the next state of a fluid given its current state.

Instead of repeatedly solving expensive numerical equations, WALRUS learns the underlying dynamics directly from data.

This dramatically speeds up prediction while maintaining impressive accuracy.

How It Works

The workflow is surprisingly straightforward:

Simulation Data
        │
        ▼
Pre-trained WALRUS Model
        │
        ▼
Predict Next Fluid State
        │
        ▼
Repeat Prediction
        │
        ▼
Future Fluid Behaviour

Like ChatGPT predicts the next word, WALRUS predicts the next physical state.

Let’s Build It

👉 The complete WALRUS fluid dynamics prediction code is available on my GitHub and can be run in environments like Google Colab.

In this project we will:

1. Load a pre-trained WALRUS model.
2. Download sample simulation data.
3. Run inference.
4. Visualize predicted flow fields.
5. Compare predictions with ground truth.

Experiment Yourself

Once the notebook works, try these modifications:

• Increase the prediction horizon.
• Compare different initial conditions.
• Measure inference speed.
• Visualize prediction error.
• Experiment with different datasets.

This is where real learning happens.

Real-World Applications

AI-based fluid prediction is already finding applications in:

• Aerospace design
• Automotive aerodynamics
• Weather forecasting
• Climate modeling
• Renewable energy
• Medical blood-flow analysis
• Robotics

As models continue improving, engineers will increasingly combine traditional physics with AI to accelerate design and discovery.

Key Takeaways

In this tutorial, you learned that:

• AI can accelerate scientific simulations.
• Foundation models are no longer limited to language.
• Physics and machine learning complement each other rather than compete.
• Pre-trained scientific models make advanced research accessible to beginners.

What’s Next?

In the next tutorial, we’ll explore another scientific foundation model and build an AI project that predicts complex physical systems with just a few lines of Python.