4 papers
A Comparative Investigation of Thermodynamic Structure-Informed Neural Networks
Guojie Li, Liu Hong
Physics-informed neural networks (PINNs) offer a unified framework for solving both forward and inverse problems of differential equations, yet their performance and physical consi…
Incorporating Continuous Dependence Qualifies Physics-Informed Neural Networks for Operator Learning
Guojie Li, Wuyue Yang, Liu Hong
Physics-informed neural networks (PINNs) have been proven as a promising way for solving various partial differential equations, especially high-dimensional ones and those with irr…
Every Step Evolves: Scaling Reinforcement Learning for Trillion-Scale Thinking Model
Ling Team, Anqi Shen, Baihui Li +101
We present Ring-1T, the first open-source, state-of-the-art thinking model with a trillion-scale parameter. It features 1 trillion total parameters and activates approximately 50 b…
MEP-Net: Generating Solutions to Scientific Problems with Limited Knowledge by Maximum Entropy Principle
Wuyue Yang, Liangrong Peng, Guojie Li +1
Maximum entropy principle (MEP) offers an effective and unbiased approach to inferring unknown probability distributions when faced with incomplete information, while neural networ…