A. A. Ramabathiran

发表

Automatic finite element formulation and assembly of hyperelastic higher order structural models

S. Gopalakrishnan, A. A. Ramabathiran, 2014 .

Anisotropic, Sparse and Interpretable Physics-Informed Neural Networks for PDEs

P. Ramachandran, A. A. Ramabathiran, 2022, ArXiv.

An iterative scheme for the generalized Peierls–Nabarro model based on the inverse Hilbert transform

A. A. Ramabathiran, 2019, 1907.01165.

Anisotropic, Sparse and Interpretable Physics-Informed Neural Networks for PDEs

P. Ramachandran, A. A. Ramabathiran, 2022, ArXiv.

Dynamic behavior of nano-voids in magnesium under hydrostatic tensile stress

M. Ortiz, K. Bhattacharya, M. Ponga, 2016 .

Comparative study of different nonconserving time integrators for wave propagation in hyperelastic waveguides

A. A. Ramabathiran, S. Gopalakrishnan, 2014, Acta Mechanica.

Comparative study of different nonconserving time integrators for wave propagation in hyperelastic waveguides

S. Gopalakrishnan, A. A. Ramabathiran, 2014 .

SPINN: Sparse, Physics-based, and Interpretable Neural Networks for PDEs pdf

Prabhu Ramachandran, Amuthan A. Ramabathiran, P. Ramachandran, 2021, ArXiv.

SPINN: Sparse, Physics-based, and partially Interpretable Neural Networks for PDEs

Prabhu Ramachandran, Amuthan A. Ramabathiran, P. Ramachandran, 2021, J. Comput. Phys..

Anisotropic, Sparse and Interpretable Physics-Informed Neural Networks for PDEs

P. Ramachandran, A. A. Ramabathiran, 2022, ArXiv.

Time and Frequency Domain Finite Element Models for Axial Wave Analysis in Hyperelastic Rods

Srinivasan Gopalakrishnan, Amuthan Arunkumar Ramabathiran, S. Gopalakrishnan, 2012 .

Galerkin Finite Element schemes for axial waves in nonlinear elastic rods

S. Gopalakrishnan, A. A. Ramabathiran, 2009 .