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Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling., , , , and . Mach. Learn. Sci. Technol., 3 (4): 45003 (December 2022)Scientific machine learning for closure models in multiscale problems: a review., , , and . CoRR, (2024)Deploying deep learning in OpenFOAM with TensorFlow., , , , and . CoRR, (2020)Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles., , , and . CoRR, (2023)AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification., , , , , and . ICPR, page 1908-1914. IEEE, (2022)Data-Driven Wind Turbine Wake Modeling via Probabilistic Machine Learning., , , and . CoRR, (2021)Interpretable Fine-Tuning for Graph Neural Network Surrogate Models., and . CoRR, (2023)Distributed deep reinforcement learning for simulation control., and . Mach. Learn. Sci. Technol., 2 (2): 25029 (2021)A Machine-Learning-Based Importance Sampling Method to Compute Rare Event Probabilities., , , and . ICCS (6), volume 12142 of Lecture Notes in Computer Science, page 169-182. Springer, (2020)Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning., , and . CoRR, (2024)