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Deep learning observables in computational fluid dynamics., , and . CoRR, (2019)SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models., , , and . CoRR, (2024)Error analysis for physics informed neural networks (PINNs) approximating Kolmogorov PDEs., and . CoRR, (2021)wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws., , and . CoRR, (2022)Error analysis for deep neural network approximations of parametric hyperbolic conservation laws., and . CoRR, (2022)Convergence of Godunov type methods for a conservation law with a spatially varying discontinuous flux function., , and . Math. Comput., 76 (259): 1219-1242 (2007)Convergence of Upwind Finite Difference Schemes for a Scalar Conservation Law with Indefinite Discontinuities in the Flux Function.. SIAM J. Numerical Analysis, 43 (2): 559-577 (2005)Generic bounds on the approximation error for physics-informed (and) operator learning., and . NeurIPS, (2022)UnICORNN: A recurrent model for learning very long time dependencies., and . ICML, volume 139 of Proceedings of Machine Learning Research, page 9168-9178. PMLR, (2021)Automatic Identification of Mixed Retinal Cells in Time-Lapse Fluorescent Microscopy Images using High-Dimensional DBSCAN., , , , , , , , and . EMBC, page 4783-4786. IEEE, (2019)