3 papers
cs.AI2026
OmniFysics-Nano-V2 Technical Report: Understanding the Physical World Across Modalities
Yizhou Liu, Jinghang Han, Kaixiang Qiu +8
Omni-modal models have expanded multimodal interaction across vision, audio, speech, and language. However, their training is predominantly organized around semantic descriptions a…
cs.LG2026
Continual-Learning Physics-Informed Neural Networks for Parameterized Partial Differential Equations
Xujia Chen, Xinyue Hu, Letian Chen +2
Physics-informed neural networks (PINNs) incorporate governing equations into neural-network training and can approximate PDE solutions without requiring large observational datase…
cs.LG2026
Curriculum Learning of Physics-Informed Neural Networks based on Spatial Correlation
Xujia Chen, Xinyue Hu, Letian Chen +2
Physics-Informed Neural Networks (PINNs) combine deep learning with physical constraints for solving partial differential equations (PDEs), and are widely applied in fluid mechanic…