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Machine Learning for the Geosciences: Challenges and Opportunities., , , , and . IEEE Trans. Knowl. Data Eng., 31 (8): 1544-1554 (2019)Rethinking the Importance of Sampling in Physics-informed Neural Networks., , , , and . CoRR, (2022)A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis., , , , , , , , , and 4 other author(s). CoRR, (2023)Incorporating Prior Domain Knowledge into Deep Neural Networks., , , , and . IEEE BigData, page 36-45. IEEE, (2018)Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling., , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 7264-7302. PMLR, (2023)Learning Physics-guided Neural Networks with Competing Physics Loss: A Summary of Results in Solving Eigenvalue Problems., , , , , , , and . AAAI Spring Symposium: MLPS, volume 2964 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)Joint sparse auto-encoder: A semi-supervised spatio-temporal approach in mapping large-scale croplands., , , , and . IEEE BigData, page 1173-1182. IEEE Computer Society, (2017)Process Guided Deep Learning for Modeling Physical Systems: An Application in Lake Temperature Modeling., , , , , , and . IGARSS, page 3494-3496. IEEE, (2020)Spatio-Temporal Data Mining: A Survey of Problems and Methods., , and . ACM Comput. Surv., 51 (4): 83:1-83:41 (2018)Predictive Learning in the Presence of Heterogeneity and Limited Training Data., , , and . SDM, page 253-261. SIAM, (2014)