January 05, 2020 conference-paper Deep Gaussian Process Enabled Surrogate Models for Aerodynamic Flows AIAA SciTech Forum DOI: 10.2514/6.2020-1640 OpenAlex Authors 0 Author list not loaded yet. Cited by 2 stored of 19 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks 2025 IEEE Congress on Evolutionary Computation (CEC) conference-paper Computer Science Advanced Multi-Objective Optimization Algorithms 1 citations Heterogeneous data-driven aerodynamic modeling based on physical feature embedding 2023 Chinese Journal of Aeronautics article Physics and Astronomy Model Reduction and Neural Networks Open access 39 citations 2 results References 0