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cs.LG2026
Physics-Informed Time-Integrated DeepONet: Temporal Tangent Space Operator Learning for High-Accuracy Inference
Luis Mandl, Dibyajyoti Nayak, Tim Ricken +1
Accurately modeling and inferring solutions to time-dependent partial differential equations (PDEs) over extended horizons remains a core challenge in scientific machine learning.…
cs.LG2024
Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning
Luis Mandl, Somdatta Goswami, Lena Lambers +1
The deep operator network (DeepONet) is a popular neural operator architecture that has shown promise in solving partial differential equations (PDEs) by using deep neural networks…