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stat.ML2025
Deep Intrinsic Coregionalization Multi-Output Gaussian Process Surrogate with Active Learning
Chun-Yi Chang, Chih-Li Sung
Deep Gaussian Processes (DGPs) are powerful surrogate models known for their flexibility and ability to capture complex functions. However, extending them to multi-output settings…
stat.ME2025
Diffusion Non-Additive Model for Multi-Fidelity Simulations with Tunable Precision
Junoh Heo, Romain Boutelet, Wenjia Wang +1
Computer simulations are indispensable for analyzing complex systems, yet high-fidelity models often incur prohibitive computational costs. Multi-fidelity frameworks address this c…
stat.ME2025
Active Learning with Adaptive Non-Stationary Kernel for Continuous-Fidelity Surrogate Models
Romain Boutelet, Chih-Li Sung
Simulating complex physical processes across a domain of input parameters can be very computationally expensive. Multi-fidelity surrogate modeling can resolve this issue by integra…