Pre-Training for Simulation-Based Science: A Study on Jet Foundation Model Training Objectives
arXiv:2606.14870v1 Announce Type: cross Abstract: Foundation models (FMs) trained on large datasets and fine-tuned on downstream tasks have emerged as a powerful paradigm in AI for science. Industrial FMs are typically trained using self-supervision with masking due to the lack of labels. In many scientific domains, accurate simulations are plentiful and facilitate large, labeled datasets. This opens up new possibilities for pre-training. We present a systematic comparison of pre-training methods using the OmniLearned High Energy Physics FM framework. We test supervised classification, flow-ma
The proliferation of foundation models in various scientific domains, particularly AI for science, is driving research into optimal pre-training methods as these models mature.
This research provides a systematic comparison of pre-training methods for foundation models in scientific simulation, which could significantly accelerate scientific discovery by improving AI's ability to model complex systems.
The understanding of how to best leverage abundant simulation data for pre-training scientific foundation models is evolving, potentially leading to more efficient and powerful AI tools in scientific research.
- · High Energy Physics Research
- · AI for Science Sector
- · Simulation Software Developers
- · Scientific Computing
- · Traditional Analytical Methods (in some areas)
- · Small Research Labs (without access to large datasets)
Improved accuracy and efficiency of AI models in scientific simulation tasks.
Faster discovery of new materials, drugs, or physical phenomena due to enhanced simulation capabilities.
The democratization of advanced scientific research as complex simulations become more accessible and interpretable via AI.
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