Sparse mixture-of-experts routing can solve the problem of conflicting physics domains in foundation models by automatically routing different physics problems to specialized experts while maintaining shared knowledge for universal principles.
This paper tackles negative transfer in multi-physics AI models—where training on different physics problems simultaneously hurts performance. The authors propose Shodh-MoE, which uses sparse expert routing to let different parts of the model specialize in different physics regimes (like fluid dynamics vs. porous media flows) while sharing knowledge where it helps.