13 citations · 25 across the 16 of their papers we have counts for
Showing 2024Show all
2 papers · 1 filter
cs.LG2024★ 1 cited
FB-HyDON: Parameter-Efficient Physics-Informed Operator Learning of Complex PDEs via Hypernetwork and Finite Basis Domain Decomposition
Milad Ramezankhani, Rishi Yash Parekh, Anirudh Deodhar +1
Deep operator networks (DeepONet) and neural operators have gained significant attention for their ability to map infinite-dimensional function spaces and perform zero-shot super-r…
cs.LG2024★ 13 cited
An Advanced Physics-Informed Neural Operator for Comprehensive Design Optimization of Highly-Nonlinear Systems: An Aerospace Composites Processing Case Study
Milad Ramezankhani, Anirudh Deodhar, Rishi Yash Parekh +1
Deep Operator Networks (DeepONets) and their physics-informed variants have shown significant promise in learning mappings between function spaces of partial differential equations…