Explore a distinguished lecture on deep learning applications in partial differential equations (PDEs) and high-dimensional computations. Delve into topics such as uncertainty quantification, supervised learning with deep neural networks, PDE constrained optimization, and operator learning. Gain insights on refined error estimates, training on low-discrepancy sequences, and bounds on reconstruction, encoding, and approximation errors. Examine real-world applications like tsunami modeling in the Mediterranean Sea and learn about challenges in out-of-distribution evaluations. Participate in a live interactive session with the speaker, where you can submit questions in advance through a provided Google form.