Dimensions overall score 5.0

GitHub Code Pulse

No public code linked for this paper yet.

• The proposed variational LSTM with augmented inputs can simultaneously capture both aleatoric and epistemic uncertainties in nonlinear dynamic structural systems.method 95%
• The method treats key random system parameters as augmented inputs alongside excitation series to capture the full range of aleatoric uncertainty.technical 90%
• Epistemic uncertainty is approximated via the Monte Carlo dropout scheme in the proposed method.technical 90%
• The proposed method incurs negligible additional training costs compared to computationally expensive full Bayesian approaches.technical 85%
• The calibrated metamodels accurately reproduce nonlinear response time histories for stochastic seismic or wind excitations.result 85%
• The method provides confidence bounds that indicate the associated epistemic uncertainty.result 85%
• Machine learning metamodels are needed to alleviate the heavy computational demands of uncertainty propagation in high-dimensional nonlinear dynamic structural systems.technical 80%
• The 'black box' nature of machine learning models creates a need to estimate epistemic uncertainty in addition to aleatoric uncertainty.technical 80%

Competitive landscape

Competitor map is still being generated for this paper. Enable generation or check back soon.

Startup potential card