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Meta-Model Framework for Surrogate-Based Parameter Estimation in Dynamical Systems., , , and . IEEE Access, (2019)Inductive Process Modeling of Rab5-Rab7 Conversion in Endocytosis., , , and . Discovery Science, volume 8140 of Lecture Notes in Computer Science, page 265-280. Springer, (2013)General Meta-Model Framework for Surrogate-Based Numerical Optimization., , , and . DS, volume 10558 of Lecture Notes in Computer Science, page 51-66. Springer, (2017)Multi-level information fusion for learning a blood pressure predictive model using sensor data., , , , , and . Inf. Fusion, (2020)Learning Surrogates of a Radiative Transfer Model for the Sentinel 5P Satellite., , , , and . DS, volume 12323 of Lecture Notes in Computer Science, page 217-230. Springer, (2020)DOT: Fast Cell Type Decomposition of Spatial Omics by Optimal Transport., , , , and . CoRR, (2023)Learning stochastic process-based models of dynamical systems from knowledge and data., , and . BMC Syst. Biol., (2016)Process-Based Modeling and Design of Dynamical Systems., , , and . ECML/PKDD (3), volume 10536 of Lecture Notes in Computer Science, page 378-382. Springer, (2017)Decoupling approximation robustly reconstructs directed dynamical networks., , , , , and . CoRR, (2017)Surrogate models of radiative transfer codes for atmospheric trace gas retrievals from satellite observations., , , , and . Mach. Learn., 112 (4): 1337-1363 (April 2023)