33 citations · 38 across the 8 of their papers we have counts for
8 papers
Minimal chaotic models from the Volterra gyrostat
Ashwin K Seshadri, S Lakshmivarahan
Low-order models obtained through Galerkin projection of several physically important systems (e.g., Rayleigh-Bénard convection, mid-latitude quasi-geostrophic dynamics, and vortic…
On the dual advantage of placing observations through forward sensitivity analysis
Shady E Ahmed, Omer San, Sivaramakrishnan Lakshmivarahan +1
The four-dimensional variational data assimilation methodology for assimilating noisy observations into a deterministic model has been the workhorse of forecasting centers for over…
Invariants and chaos in the Volterra gyrostat without energy conservation
Ashwin K Seshadri, S Lakshmivarahan
The model of the Volterra gyrostat (VG) has not only played an important role in rigid body dynamics but also served as the foundation of low-order models of many naturally occurri…
Forward sensitivity analysis of the FitzHugh-Nagumo system: Parameter estimation
Shady E. Ahmed, Omer San, Sivaramakrishnan Lakshmivarahan
The FitzHugh-Nagumo (FHN) model, from computational neuroscience, has attracted attention in nonlinear dynamics studies as it describes the behavior of excitable systems and exhibi…
Applying a random projection algorithm to optimize machine learning model for breast lesion classification
Morteza Heidari, Sivaramakrishnan Lakshmivarahan, Seyedehnafiseh Mirniaharikandehei +4
Machine learning is widely used in developing computer-aided diagnosis (CAD) schemes of medical images. However, CAD usually computes large number of image features from the target…
Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images
Seyedehnafiseh Mirniaharikandehei, Morteza Heidari, Gopichandh Danala +2
Background and Objective: Non-invasively predicting the risk of cancer metastasis before surgery plays an essential role in determining optimal treatment methods for cancer patient…