3 papers
cs.LG2024
A domain decomposition-based autoregressive deep learning model for unsteady and nonlinear partial differential equations
Sheel Nidhan, Haoliang Jiang, Lalit Ghule +3
In this paper, we propose a domain-decomposition-based deep learning (DL) framework, named transient-CoMLSim, for accurately modeling unsteady and nonlinear partial differential eq…
cs.LG2024
Sampling-based Distributed Training with Message Passing Neural Network
Priyesh Kakka, Sheel Nidhan, Rishikesh Ranade +2
In this study, we introduce a domain-decomposition-based distributed training and inference approach for message-passing neural networks (MPNN). Our objective is to address the cha…
physics.flu-dyn2023
Diffusion model based data generation for partial differential equations
Rucha Apte, Sheel Nidhan, Rishikesh Ranade +1
In a preliminary attempt to address the problem of data scarcity in physics-based machine learning, we introduce a novel methodology for data generation in physics-based simulation…