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Found 7 papers, 0 papers with code
Neuroscience inspired scientific machine learning (Part-1): Variable spiking neuron for regression
This property of the proposed VSN makes it suitable for regression tasks, which is a weak point for the vanilla spiking neurons, all while keeping the energy budget low.
Neuroscience inspired scientific machine learning (Part-2): Variable spiking wavelet neural operator
We propose, in this paper, a Variable Spiking Wavelet Neural Operator (VS-WNO), which aims to bridge the gap between theoretical and practical implementation of Artificial Intelligence (AI) algorithms for mechanics applications.
Randomized prior wavelet neural operator for uncertainty quantification
In this paper, we propose a novel data-driven operator learning framework referred to as the \textit{Randomized Prior Wavelet Neural Operator} (RP-WNO).
Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations
Neural network based data-driven operator learning schemes have shown tremendous potential in computational mechanics.
Assessment of DeepONet for reliability analysis of stochastic nonlinear dynamical systems
Time dependent reliability analysis and uncertainty quantification of structural system subjected to stochastic forcing function is a challenging endeavour as it necessitates considerable computational time.
Physics-integrated hybrid framework for model form error identification in nonlinear dynamical systems
For improving the predictive capability of the underlying physics, we first use machine learning algorithm to learn a mapping between the estimated state and the input (model-form error) and then introduce it into the governing equation as an additional term.
Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system
In this paper, we propose a novel digital twin framework for stochastic nonlinear multi-degree of freedom (MDOF) dynamical systems.
