Fine-Tuning AI Surrogate Models for Physics Simulations with Walrus on AMD Instinct GPU Accelerators

2026-03-06 · Source: AMD ROCm Blogs · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Emerging Technologies & Innovation · Depth: Intermediate, long

Summary

AMD demonstrated fine-tuning the Walrus 1.3B parameter foundation model for physics simulations on AMD Instinct MI300X GPU accelerators. Walrus is a transformer-based architecture pretrained on 19 diverse 2D and 3D simulated datasets with 63 state variables, enabling cross-domain knowledge transfer for continuum dynamics. Its architecture features space-time factorized attention, axial rotary position embeddings for spatial attention, and T5-style relative position embeddings for temporal attention, along with patch jittering and compute-adaptive compression for stability and efficiency across varying spatial resolutions. The fine-tuning process, using the `post_neutron_star_merger` dataset from The Well, involved setting up a Docker environment, downloading pretrained checkpoints and the 110.1 GB dataset, and running a distributed training job. This approach significantly reduces the computational cost of high-fidelity simulations, which can take weeks, to minutes for evaluation.

Key takeaway

For AI Engineers working with complex physics simulations, fine-tuning foundation models like Walrus on AMD Instinct MI300X GPUs offers a practical path to accelerate research and development. You can adapt broad physical priors to specific tasks, drastically reducing computational time from weeks to minutes for high-fidelity simulations. Consider integrating this workflow to rapidly explore parameter spaces and generate long rollouts for scientific workloads.

Key insights

Fine-tuning foundation models like Walrus significantly improves physics simulation accuracy and efficiency on AMD GPUs.

Principles

• Foundation models generalize across diverse physical systems.
• Fine-tuning adapts broad priors to specific tasks.
• Factorized attention improves spatiotemporal sequence modeling.

Method

The method involves downloading a pretrained Walrus model and a target dataset, then fine-tuning the model using a Dockerized environment on AMD Instinct MI300X GPUs, followed by evaluation and visualization.

In practice

• Use AMD Instinct MI300X for scientific AI workloads.
• Fine-tune Walrus on specific physics datasets.
• Employ Docker for environment setup and reproducibility.

Topics

• AI Surrogate Models
• Walrus Foundation Model
• Physics Simulations
• AMD Instinct MI300X
• Model Fine-tuning

Code references

• PolymathicAI/the_well
• ROCm/rocm-blogs

Best for: Machine Learning Engineer, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AMD ROCm Blogs.