TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers

1 code implementation • 5 Jul 2023 • Alan John Varghese, Aniruddha Bora, Mengjia Xu, George Em Karniadakis

Hence, incorporating long-range dependencies from the historical graph context plays a crucial role in accurately learning their temporal dynamics.

Anomaly Detection Computational Efficiency +6

Learning bias corrections for climate models using deep neural operators

no code implementations • 7 Feb 2023 • Aniruddha Bora, Khemraj Shukla, Shixuan Zhang, Bryce Harrop, Ruby Leung, George Em Karniadakis

In this study, we replace the bias correction process with a surrogate model based on the Deep Operator Network (DeepONet).

Physics-Informed Deep Neural Operator Networks

no code implementations • 8 Jul 2022 • Somdatta Goswami, Aniruddha Bora, Yue Yu, George Em Karniadakis

Standard neural networks can approximate general nonlinear operators, represented either explicitly by a combination of mathematical operators, e. g., in an advection-diffusion-reaction partial differential equation, or simply as a black box, e. g., a system-of-systems.

Uncertainty Quantification