135 citations · 541 across the 18 of their papers we have counts for
4 papers · 1 filter
Multi-fidelity information fusion with concatenated neural networks
Suraj Pawar, Omer San, Prakash Vedula +2
Recently, computational modeling has shifted towards the use of deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many appl…
Deep neural network enabled corrective source term approach to hybrid analysis and modeling
Sindre Stenen Blakseth, Adil Rasheed, Trond Kvamsdal +1
In this work, we introduce, justify and demonstrate the Corrective Source Term Approach (CoSTA) -- a novel approach to Hybrid Analysis and Modeling (HAM). The objective of HAM is t…
Model fusion with physics-guided machine learning
Suraj Pawar, Omer San, Aditya Nair +2
The unprecedented amount of data generated from experiments, field observations, and large-scale numerical simulations at a wide range of spatio-temporal scales have enabled the ra…
Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution
Omer San, Adil Rasheed, Trond Kvamsdal
Most modeling approaches lie in either of the two categories: physics-based or data-driven. Recently, a third approach which is a combination of these deterministic and statistical…