arXiv:2606.19781v1 Announce Type: cross Abstract: Neural scaling laws describe how model performance improves as a power law in compute, model size, and dataset size. While well-established for large language models, these relationships are emerging for large models in particle physics. As with language, empirical studies show that the performance scales as a power law. However, unlike natural language or image domains, fundamental physics has high-fidelity simulators that produce synthetic data cheaply. This favors scaling regimes where additional data is cheaper than additional parameters, a
The proliferation of Large Language Models (LLMs) has highlighted the importance of scaling laws, and this research indicates a new application of these principles to physics with unique data characteristics.
Understanding and engineering scaling laws for scientific domains like particle physics could dramatically accelerate discovery and reduce the enormous compute costs associated with traditional high-fidelity simulations.
The optimization strategy for developing large models in scientific fields shifts from solely focusing on parameter count to emphasizing efficient data generation and composition, particularly synthetic data.
- · High-energy physics researchers
- · Generative AI data companies
- · Specialized scientific computing platforms
- · Traditional high-fidelity simulator developers (if not integrated with AI)
- · Organizations beholden to brute-force compute scaling
Scientific domains with high-fidelity simulators will see accelerated AI model development.
This could lead to breakthroughs in fundamental physics or materials science as models become more powerful and efficient.
The methodology might eventually generalize to other data-rich simulation environments, democratizing access to complex modeling.
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Read at arXiv cs.AI