3 papers
physics.flu-dyn2026
NeuralFVM: Neural-physics-based Finite Volume Method for Turbulent Flows Using the - Model
Tingkai Xue, Yu Jiao, Te Ba +9
In this work, we develop a neural-physics solver based on finite volume method (FVM), namely NeuralFVM, for turbulent flows by implementing the standard - model designed for…
cs.LG2026
Physics-Informed Uncertainty Enables Reliable AI-driven Design
Tingkai Xue, Chin Chun Ooi, Yang Jiang +5
Inverse design is a central goal in much of science and engineering, including frequency-selective surfaces (FSS) that are critical to microelectronics for telecommunications and o…
physics.comp-ph2025
Differentiable Physics-Neural Models enable Learning of Non-Markovian Closures for Accelerated Coarse-Grained Physics Simulations
Tingkai Xue, Chin Chun Ooi, Zhengwei Ge +3
Numerical simulations provide key insights into many physical, real-world problems. However, while these simulations are solved on a full 3D domain, most analysis only require a re…