Referencias

Lista no exhaustiva de posibles artículos a revisar.

• Antonelo, E. A., Camponogara, E., Seman, L. O., Jordanou, J. P., De Souza, E. R., & Hübner, J. F. (2024). Physics-informed neural nets for control of dynamical systems. Neurocomputing, 579, 127419. https://doi.org/10.1016/j.neucom.2024.127419 (será presentado por Rodrigo Baliosian, Facundo Gil y Marcos Ibarburu)
• Brunton, S. L., & Kutz, J. N. (2022). Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control (2nd ed.). Cambridge University Press. https://www.cambridge.org/highereducation/product/9781009089517/book
• Burns, Z., & Liu, Z. (2023). Untrained, physics-informed neural networks for structured illumination microscopy. Optics Express, 31(5), 8714. https://doi.org/10.1364/OE.476781
• Cobelli, P., Shukla, K., Nesmachnow, S., & Draper, M. (2023). Physics informed neural networks for wind field modeling in wind farms. Journal of Physics: Conference Series, 2505(1), 012051. https://doi.org/10.1088/1742-6596/2505/1/012051
• Gupta, H., McCann, M. T., Donati, L., & Unser, M. (2021). CryoGAN: A New Reconstruction Paradigm for Single-Particle Cryo-EM Via Deep Adversarial Learning. IEEE Transactions on Computational Imaging, 7, 759–774. https://doi.org/10.1109/TCI.2021.3096491
• Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707. https://doi.org/10.1016/j.jcp.2018.10.045
• Thuerey, N., Holl, P., Mueller, M., Schnell, P., Trost, F., & Um, K. (2021). Physics-based Deep Learning. WWW. https://physicsbaseddeeplearning.org
• Xypakis, E., De Turris, V., Gala, F., Ruocco, G., & Leonetti, M. (2023). Physics-informed deep neural network for image denoising. Optics Express, 31(26), 43838. https://doi.org/10.1364/OE.504606
• Ye, Z., Huang, Y., Zhang, J., Chen, Y., Ye, H., Ji, C., Jin, L., Gan, Y., Sun, Y., Tao, W., Han, Y., Liu, X., Chen, Y., Kuang, C., & Liu, W. (2024). Universal and High-Fidelity Resolution Extending for Fluorescence Microscopy Using a Single-Training Physics-Informed Sparse Neural Network. Intelligent Computing, 3, 0082. https://doi.org/10.34133/icomputing.0082