Learning to Think in Physics: Breaking Shortcut Learning in Scientific Diffusion via Representation Alignment
arXiv:2605.20780v1 Announce Type: new Abstract: Physics-informed diffusion models typically enforce PDE constraints only on final outputs, leaving intermediate representations unconstrained and prone to shortcut learning under shifted boundary conditions. We introduce **REPA-P**, a teacher-free, architecture-agnostic framework that aligns intermediate features with physical states using first-principles residuals. REPA-P attaches lightweight $1{\times}1$ projection heads to selected layers, decodes hidden activations into physical quantities, and applies PDE residual losses during training. Th
The increasing complexity and application of AI models in scientific domains necessitate more robust and reliable methods to prevent 'shortcut learning' and ensure physical consistency, pushing research like REPA-P to emerge.
This development addresses a fundamental limitation of physics-informed AI, enhancing model reliability and interpretability, which is crucial for high-stakes scientific and engineering applications.
AI models will be able to integrate physical principles more deeply into their learning processes, moving beyond surface-level constraint enforcement to internal representation alignment, improving their robustness under novel conditions.
- · AI researchers
- · Engineering design firms
- · Scientific computing sector
- · Material science
- · Models reliant on naive physics-informed AI
- · Traditional simulation software
Improved accuracy and generalization of AI models for physical phenomena.
Accelerated discovery cycles in fields like climate modeling, drug design, or advanced materials.
Reduced need for expensive physical experiments as AI simulations become more trustworthy and predictive.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG