activity
20162023
most citedA comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations

34 citations · 82 across the 15 of their papers we have counts for

collaborators
Showing 2021Show all

2 papers · 1 filter

math.DS2021★ 1 cited

Lorenz System State Stability Identification using Neural Networks

Megha Subramanian, Ramakrishna Tipireddy, Samrat Chatterjee

Nonlinear dynamical systems such as Lorenz63 equations are known to be chaotic in nature and sensitive to initial conditions. As a result, a small perturbation in the initial condi…

math.NA2021

Time-dependent stochastic basis adaptation for uncertainty quantification

Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky

We extend stochastic basis adaptation and spatial domain decomposition methods to solve time varying stochastic partial differential equations (SPDEs) with a large number of input…