LeafNet algorithm reformulates hyperbolic conservation laws as a coupled system with smooth solutions and approximates smooth functions via physics-informed neural networks, enabling accurate computation of multidimensional hyperbolic PDEs without diffusion.
A physics-informed self-supervised learning framework that jointly performs wire calibration and interaction position reconstruction in multi-wire parallel plate avalanche counters, enhancing adaptability to changing operating conditions and detector aging.
See LICENSE fileparticle detectorwire calibrationposition reconstruction
A physics-informed dual-state query learning framework for GPCR ligand bioactivity prediction, addressing the neglect of dynamic conformational equilibria in existing deep learning approaches to improve functional efficacy discrimination.
See LICENSE fileGPCR ligandbioactivity predictionphysics-informed learning
Proposes a method to enforce hard physical constraints in generative models for semiconductor manufacturing by construction, ensuring that generated masks, layouts, and process recipes strictly comply with lithography, transport, reaction, and device-physics constraints.
See LICENSE filesemiconductor manufacturingphysics-informed learninggenerative AI
