Pre-Training for Simulation-Based Science: A Study on Jet Foundation Model Training Objectives

2026-06-12 · Source: Machine Learning · Field: Science & Research — Artificial Intelligence & Machine Learning, Physical Sciences & Chemistry · Depth: Expert, quick

Summary

A study systematically compared pre-training methods for Foundation Models (FMs) in simulation-based science, utilizing the OmniLearned High Energy Physics FM framework. Researchers evaluated supervised classification, flow-matching generation, and self-supervised masked particle modeling (MPM). Models were pre-trained on the JetClass dataset and fine-tuned on top jet classification and JetNet conditional generation tasks. Findings indicate that pure classifier pre-training excels for classification when downstream labels and model capacity are plentiful. However, combining classifier pre-training with MPM proves uniquely powerful in low-finetuning label environments. Flow matching-based generative pre-training showed minimal benefit for downstream classification. Notably, for downstream generation tasks, flow matching must be part of the pre-training objective to achieve significant fine-tuning advantages, suggesting classification and generation tasks require distinct pre-training approaches for optimal transfer.

Key takeaway

For AI Scientists developing foundation models in simulation-based science, carefully select your pre-training objectives based on target downstream tasks and label availability. If your goal is classification with limited fine-tuning data, you should combine supervised classification with masked particle modeling. Conversely, if you aim for generative capabilities, ensure flow matching is explicitly part of your pre-training strategy to achieve effective transfer. This tailored approach optimizes model performance and resource utilization.

Key insights

Pre-training objectives for scientific FMs must align with downstream tasks, especially for classification versus generation.

Principles

• Optimal pre-training depends on downstream task and label availability.
• Classification and generation tasks may require orthogonal pre-training.
• Combining supervised and self-supervised methods can mitigate label scarcity.

Method

The study systematically compared supervised classification, flow-matching generation, and self-supervised masked particle modeling pre-training methods within the OmniLearned High Energy Physics FM framework, using JetClass for pre-training and two downstream tasks for fine-tuning.

In practice

• Consider combined classifier and MPM pre-training for low-label scenarios.
• Include flow matching in pre-training for generative downstream tasks.
• Evaluate pre-training objectives based on specific downstream needs.

Topics

• Foundation Models
• Pre-training Objectives
• High Energy Physics
• Masked Particle Modeling
• Flow Matching
• Simulation Science

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.