Physics-Aligned Self-Supervised Learning for Scientific Imaging

2026-07-30

AI Summary

Standard data augmentation in self-supervised learning overlooks the symmetries and acquisition constraints dictated by the physical measurement process in scientific imaging, often leading to distorted representations. This work proposes a physics-aligned self-supervised learning framework that, for the first time, systematically incorporates measurement operator constraints into augmentation design. The approach establishes a modality-agnostic, label-light pipeline for selecting augmentations, thereby transforming augmentation into a controllable inductive bias. Evaluated across five prominent self-supervised methods—DINOv2, SimCLR, MAE, VICRegL, and I-JEPA—and applied to real-space electron microscopy and 4D-STEM datasets, the method significantly improves downstream task performance, reduces geodesic error, enhances robustness to detector gain variations and resolution loss, and reshapes the geometry of learned representations.

Abstract

Data augmentations define the invariances learned by self-supervised learning (SSL). Standard augmentation pipelines were designed for natural images, yet scientific imaging modalities are governed by physical measurement processes with distinct symmetry and acquisition constraints. Enforcing invariances that contradict these constraints can distort learned representations and limit downstream performance, but practitioners moving from machine learning into a new scientific modality currently have little guidance beyond transferring natural-image pipelines unexamined. We address this gap with a principled, reproducible procedure for augmentation design in scientific SSL: we formalise the physics-aligned augmentation set as a union of measurement-consistent symmetries and acquisition-driven perturbations, and we give a concrete, largely label-free workflow---enumerate candidates, label each by the measurement operator, validate with representation-geometry diagnostics, and confirm by single-factor ablation---for selecting them. We instantiate the procedure for real-space electron microscopy and reciprocal-space 4D-STEM diffraction, and evaluate it across five SSL paradigms (DINOv2, SimCLR, MAE, VICRegL, I-JEPA) on classification and crystal-orientation regression. Physics-aligned augmentations substantially improve downstream performance for objectives relying on cross-view consistency, reduce geodesic error and improve robustness under realistic acquisition variability (detector gain, resolution loss), and systematically reshape representation geometry. While our experiments use electron microscopy, the procedure is modality-agnostic and applies to other measurement-driven domains such as medical and remote-sensing imaging. These results position augmentation design as a primary, and controllable, source of inductive bias in scientific self-supervised learning.

Problem

• self-supervised learning
• scientific imaging
• data augmentation
• physics-aligned
• measurement constraints

Innovation

• physics-aligned augmentation
• self-supervised learning
• scientific imaging
• measurement consistency
• representation geometry