Scientific Machine Learning
- Ye D, Krzhizhanovskaya V, Hoekstra AG. Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots. Journal of Computational Physics. 2024 Jan 15;497:112639.
- Ye D, Guo M. Gaussian process learning of nonlinear dynamics. Communications in Nonlinear Science and Numerical Simulation. 2024 Nov 1;138:108184.
- Dummer S, Ye D, Brune C. RONOM: Reduced-order neural operator modeling. SIAM Journal on Scientific Computing. 2026 Jun 30;48(3):C604-34.
- Ye D, Yan W, Brune C, Guo M. PDE-constrained Gaussian process surrogate modeling with uncertain data locations. Advanced Modeling and Simulation in Engineering Sciences. 2025 Nov 8;12(1):33.
- Kevopoulos K, Ye D. A parametric framework for kernel-based dynamic mode decomposition using deep learning. Journal of Computational Science. 2026 May 20:102905.
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