#physics-ai News & Analysis

2 articles tagged with #physics-ai. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBullisharXiv – CS AI · Mar 37/105

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CHLU: The Causal Hamiltonian Learning Unit as a Symplectic Primitive for Deep Learning

Researchers propose the Causal Hamiltonian Learning Unit (CHLU), a physics-based deep learning primitive that addresses stability issues in temporal dynamics models. The CHLU uses symplectic integration and Hamiltonian structure to maintain infinite-horizon stability while preserving information, potentially solving the memory-stability trade-off in neural networks.

AINeutralarXiv – CS AI · Mar 34/105

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Physics-Consistent Diffusion for Efficient Fluid Super-Resolution via Multiscale Residual Correction

Researchers developed ReMD, a physics-consistent diffusion framework that improves fluid super-resolution by incorporating physical constraints and multiscale modeling. The approach addresses limitations of existing image and diffusion models when applied to fluid dynamics, achieving better accuracy and spectral fidelity with fewer sampling steps.