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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…
physics.comp-ph2025★ 1 cited
Hybrid Quantum Physics-informed Neural Network: Towards Efficient Learning of High-speed Flows
Fong Yew Leong, Wei-Bin Ewe, Tran Si Bui Quang +2
This study benchmarks hybrid quantum physics-informed neural network (HQPINN) to model high-speed flows, compared against classical physics-informed neural networks (PINNs) and ful…