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. arXiv preprint arXiv:2507.12814. 2025 Jul 17.

  • Ye D, Yan W, Brune C, Guo M. PDE-constrained Gaussian process surrogate modeling with uncertain data locations. arXiv preprint arXiv::2305.11586. 2023 May 19.

  • Kevopoulos K, Ye D. A parametric framework for kernel-based dynamic mode decomposition using deep learning. arXiv preprint arXiv:2409.16817. 2024 Sep 25.