22 citations · 25 across the 4 of their papers we have counts for
5 papers
Localized Operator Learning with Adaptive Partition-of-Unity Mixture-of-Expert Networks
Madison Cooley, Ramansh Sharma, Shandian Zhe +2
Operator learning methods such as DeepONets and FNOs often struggle with PDE families featuring sharp interfaces, heterogeneous coefficients, and localized multiscale structures. W…
Fluids You Can Trust: Property-Preserving Operator Learning for Incompressible Flows
Ramansh Sharma, Matthew Lowery, Houman Owhadi +1
We present a novel property-preserving kernel-based operator learning method for incompressible flows governed by the incompressible Navier--Stokes equations. Traditional numerical…
Ensemble and Mixture-of-Experts DeepONets For Operator Learning
Ramansh Sharma, Varun Shankar
We present a novel deep operator network (DeepONet) architecture for operator learning, the ensemble DeepONet, that allows for enriching the trunk network of a single DeepONet with…
Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations
Ramansh Sharma, Varun Shankar
We present a new technique for the accelerated training of physics-informed neural networks (PINNs): discretely-trained PINNs (DT-PINNs). The repeated computation of partial deriva…
Beyond modeling: NLP Pipeline for efficient environmental policy analysis
Jordi Planas, Daniel Firebanks-Quevedo, Galina Naydenova +4
As we enter the UN Decade on Ecosystem Restoration, creating effective incentive structures for forest and landscape restoration has never been more critical. Policy analysis is ne…