4 papers
An approach to encode divergence-free stress fields in neural approximations based on stress potentials
Mohammad S. Khorrami, Pawan Goyal, Soroush Motahari +5
The purpose of the current work is the development of an approach to account for quasi-static mechanical equilibrium in empirical (i.e., data-based) models for the stress field emp…
Reduced-Order Inference with Structure-Preserving Parametrization for Bending and Rotating Systems
Yevgeniya Filanova, Igor Pontes Duff, Pawan Goyal +1
Mechanical systems are often characterized only by their response to certain loads known from experiments or simulations. The obtained data can be used for various purposes: system…
On the representation of energy-preserving quadratic operators with application to Operator Inference
Leonidas Gkimisis, Igor Pontes Duff, Pawan Goyal +1
In this work, we investigate a skew-symmetric parameterization for energy-preserving quadratic operators. Earlier, [Goyal et al., 2023] proposed this parameterization to enforce en…
A physics-encoded Fourier neural operator approach for surrogate modeling of divergence-free stress fields in solids
Mohammad S. Khorrami, Pawan Goyal, Jaber R. Mianroodi +3
The purpose of the current work is the development of a so-called physics-encoded Fourier neural operator (PeFNO) for surrogate modeling of the quasi-static equilibrium stress fiel…