Physics-informed neural operators with exact differentiation on arbitrary geometries
Neural Operators can learn operators from data, for example, to solve partial differential
equations (PDEs). In some cases, this data-driven approach is not sufficient, eg, if the data is …
equations (PDEs). In some cases, this data-driven approach is not sufficient, eg, if the data is …
Graphon Mean Field Games with A Representative Player: Analysis and Learning Algorithm
We propose a discrete-time graphon game formulation on continuous state and action
spaces using a representative player to study stochastic games with heterogeneous …
spaces using a representative player to study stochastic games with heterogeneous …
Physics-Embedded Deep Learning to Predict Real-Time Flow Parameters in Complex Thermodynamic Machinery
Z Lin, D Xiao, H Xiao - Aerospace, 2024 - mdpi.com
Flow through complex thermodynamic machinery is intricate, incorporating turbulence,
compressibility effects, combustion, and solid–fluid interactions, posing a challenge to …
compressibility effects, combustion, and solid–fluid interactions, posing a challenge to …
Physics-Informed Graph Neural Operator for Mean Field Games on Graph: A Scalable Learning Approach
Mean-field games (MFGs) are developed to model the decision-making processes of a large
number of interacting agents in multi-agent systems. This paper studies mean-field games …
number of interacting agents in multi-agent systems. This paper studies mean-field games …