arXiv:2509.13805v4 Announce Type: replace Abstract: Foundation models have revolutionized natural language processing through a ``train once, deploy anywhere'' paradigm, where a single pre-trained model adapts to countless downstream tasks without retraining. Access to a Physics Foundation Model (PFM) would be transformative - democratizing access to high-fidelity simulations, accelerating scientific discovery, and eliminating the need for specialized solver development. Yet current physics-aware machine learning approaches remain fundamentally limited to single, narrow domains and require ret
The concept of foundation models has matured significantly in language processing, making their extension to other complex domains like physics a natural next step in AI research.
A Physics Foundation Model (PFM) promises to democratize high-fidelity simulations and accelerate scientific discovery across numerous fields, reducing the need for specialized, domain-specific AI development.
The development of PFMs could fundamentally alter how scientific research and engineering simulations are conducted, moving from bespoke models to adaptable, pre-trained systems.
- · Scientific research institutions
- · Engineering sectors
- · AI model developers
- · Academic researchers
- · Developers of highly specialized domain-specific physics solvers
- · Traditional simulation software companies
Rapid acceleration of discovery in materials science, drug development, and climate modeling due to accessible high-fidelity simulations.
Reduced barriers to entry for complex scientific or engineering problems, enabling smaller teams or even individuals to conduct advanced research.
The emergence of new industries and products based on previously intractable simulation challenges, potentially leading to unforeseen technological leaps.
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