arXiv:2602.20399v2 Announce Type: replace Abstract: Neural simulators promise efficient surrogates for physics simulation, but scaling them is bottlenecked by the prohibitive cost of generating high-fidelity training data. Pre-training on abundant off-the-shelf geometries offers a natural alternative, yet faces a fundamental gap: supervision on static geometry alone ignores dynamics and can lead to negative transfer on physics tasks. We present GeoPT, a unified pre-trained model for general physics simulation based on lifted geometric pre-training. The core idea is to augment geometry with syn
The increasing computational demands of scaling AI models for complex tasks like physics simulation necessitate more efficient data generation and training methods.
Efficient physics simulation is critical for advancing robotics, material science, and engineering, enabling faster R&D cycles and more realistic virtual testing environments.
The ability to pre-train physics simulation models on static geometry and generalize to dynamics significantly reduces the cost and time required to develop high-fidelity simulators.
- · AI research labs
- · Robotics companies
- · Engineering and design firms
- · Gaming and metaverse developers
- · Traditional physics simulation software requiring extensive dynamic data sets
- · Companies reliant on slow, computationally expensive simulation processes
Artificial intelligence applications requiring accurate physical interactions will become more accessible and performant.
The development of more agile and capable AI agents and physical robots will accelerate due to improved simulation environments.
This could lead to a 'simulation singularity' where the cost of generating new physical data for models approaches zero, dramatically speeding up scientific discovery and engineering.
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